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The uMR Jupiter system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces sagittal, transverse, coronal, and oblique cross sectional images, and spectroscopic images, and that display internal anatomical structure and/or function of the head, body and extremities.

These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist the diagnosis. Contrast agents may be used depending on the region of interest of the scan.

The device is intended for patients > 20 kg/44 lbs.

uMR Jupiter is a 5T superconducting magnetic resonance diagnostic device with a 60cm size patient bore and 8 channel RF transmit system. It consists of components such as magnet, RF power amplifier, RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, and vital signal module etc. uMR Jupiter is designed to conform to NEMA and DICOM standards.

The modification performed on the uMR Jupiter in this submission is due to the following changes that include:

1. Addition of RF coils: SuperFlex Large - 24 and Foot & Ankle Coil - 24.

2. Addition of applied body part for certain coil: SuperFlex Small-24 (add imaging of ankle).

3. Addition and modification of pulse sequences:

   • a) New sequences: fse_wfi, gre_fsp_c (3D), gre_bssfp_ucs, epi_fid(3D), epi_dti_msh.
   • b) Added Associated options for certain sequences: asl_3d (add mPLD) (Only output original images and no quantification images are output), gre_fsp_c (add Cardiac Cine, Cardiac Perfusion, PSIR, Cardiac mapping), gre_quick(add WFI, MRCA), gre_bssfp(add Cardiac Cine, Cardiac mapping), epi_dwi(add IVIM) (Only output original images and no quantification images are output).

4. Addition of function: EasyScan, EasyCrop, t-ACS, QScan, tFAST, DeepRecon and WFI.

5. Addition of workflow: EasyFACT.

This FDA 510(k) summary (K250246) for the uMR Jupiter provides details on several new AI-assisted features. Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Important Note: The document is not a MRMC study comparing human readers with and without AI. Instead, it focuses on the performance of individual AI modules and their integration into the MRI system, often verified by radiologists' review of image quality.

Acceptance Criteria and Reported Device Performance

The document presents acceptance criteria implicitly through the "Test Result" or "Performance Verification" sections for each AI feature. The "Performance" column below summarizes the device's reported achievement for these criteria.

1. "A better consistency between t-ACS and the reference than that between CS and the reference was shown in all t-ACS application types."
2. "No large structural difference appeared between t-ACS and the reference in all t-ACS application types."
3. "The motion-time curves and Bland-Altman analysis showed the consistency between t-ACS and the reference based on simulated and real acquired data in all t-ACS application types."
   Overall: "The t-ACS on uMR Jupiter was shown to perform better than traditional Compressed Sensing in the sense of discrepancy from fully sampled images and PSNR using images from various age groups, BMIs, ethnicities and pathological variations. The structure measurements on paired images verified that same structures of t-ACS and reference were significantly the same. And t-ACS integration tests in two applications proved that t-ACS had good agreement with the reference." |
   | DeepRecon | Expected to provide image de-noising and super-resolution, resulting in diagnostic quality images, with equivalent or higher scores than reference images in terms of diagnostic quality. | "The DeepRecon has been validated to provide image de-nosing and super-resolution processing using various ethnicities, age groups, BMIs, and pathological variations. In addition, DeepRecon images were evaluated by American Board of Radiologists certificated physicians, covering a range of protocols and body parts. The evaluation reports from radiologists verified that DeepRecon meets the requirements of clinical diagnosis. All DeepRecon images were rated with equivalent or higher scores in terms of diagnosis quality." |
   | EasyFACT | Expected to effectively automate ROI placement and numerical statistics for FF and R2* values, with results subjectively evaluated as effective. | "The subjective evaluation method was used [to verify effectiveness]." "The proposal of algorithm acceptance criteria and score processing are conducted by the licensed physicians with U.S. credentials." (Implied successful verification from context) |
   | EasyScan | Pass criteria of 99.3% for automatic slice group positioning, meeting safety and effectiveness requirements. | "The pass criteria of EasyScan feature is 99.3%, and the results evaluated by the licenced MRI technologist with U.S. credentials. Therefore, EasyScan meets the criteria for safety and effectiveness, and EasyScan can meet the requirements for automatic positioning locates slice groups." (Implied reaching or exceeding 99.3%.) |
   | EasyCrop | Pass criteria of 100% for automatic image cropping, meeting safety and effectiveness requirements. | "The pass criteria of EasyCrop feature is 100%, and the results evaluated by the licenced MRI technologist with U.S. credentials. Therefore, EasCrop meets the criteria for safety and effectiveness, and EasCrop can meet the requirements for automatic cropping." (Implied reaching or exceeding 100%.) |

Study Details

1. Sample sizes used for the test set and the data provenance:

   • WFI: 144 cases from 28 volunteers. Data collected from UIH Jupiter. "Completely separated from the previous mentioned training dataset by collecting from different volunteers and during different time periods." (Retrospective for testing, though specific country of origin beyond "UIH MRI systems" is not explicitly stated for testing data, training data has "Asian" majority.)
   • t-ACS: 35 subjects (data from 76 volunteers used for overall training/validation/test split). Test data collected independently from the training data, with separated subjects and during different time periods. "White," "Black," and "Asian" ethnicities mentioned, implying potentially multi-country or diverse internal dataset.
   • DeepRecon: 20 subjects (2216 cases). "Diverse demographic distributions" including "White" and "Asian" ethnicities. "Collecting testing data from various clinical sites and during separated time periods."
   • EasyFACT: 5 subjects. "Data were acquired from 5T magnetic resonance imaging equipment from UIH," and "Asia" ethnicity is listed.
   • EasyScan: 30 cases from 18 "Asia" subjects (initial testing); 40 cases from 8 "Asia" subjects (validation on uMR Jupiter system).
   • EasyCrop: 5 subjects. "Data were acquired from 5T magnetic resonance imaging equipment from UIH," and "Asia" ethnicity is listed.

   Data provenance isn't definitively "retrospective" or "prospective" for the test sets, but the emphasis on "completely separated" and "independent" from training data collected at "different time periods" suggests these were distinct, potentially newly acquired or curated sets for evaluation. The presence of multiple ethnicities (White, Black, Asian) suggests potentially broader geographical origins than just China where the company is based, or a focus on creating diverse internal datasets.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • WFI: Three U.S. certificated radiologists. (Qualifications: U.S. board-certified radiologists).
   • t-ACS: No separate experts establishing ground truth for the test set performance evaluation are mentioned beyond the quantitative metrics (MAE, PSNR, SSIM) compared against "fully sampled images" (reference/ground truth). The document states that fully-sampled k-space data transformed into image domain served as the reference.
   • DeepRecon: American Board of Radiologists certificated physicians. (Qualifications: American Board of Radiologists certificated physicians).
   • EasyFACT: Licensed physicians with U.S. credentials. (Qualifications: Licensed physicians with U.S. credentials).
   • EasyScan: Licensed MRI technologist with U.S. credentials. (Qualifications: Licensed MRI technologist with U.S. credentials).
   • EasyCrop: Licensed MRI technologist with U.S. credentials. (Qualifications: Licensed MRI technologist with U.S. credentials).

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   The document does not explicitly state an adjudication method (like 2+1 or 3+1) for conflict resolution among readers. For WFI, DeepRecon, EasyFACT, EasyScan, and EasyCrop, it implies a consensus or majority opinion model based on the "evaluation reports from radiologists/technologists." For t-ACS, the evaluation of the algorithm's output is based on quantitative metrics against a reference image ground truth.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

   No, a traditional MRMC comparative effectiveness study where human readers interpret cases with AI assistance versus without AI assistance was not described. The studies primarily validated the AI features' standalone performance (e.g., image quality, accuracy of automated functions) or their output's equivalence/superiority to traditional methods, often through expert review of the AI-generated images. Therefore, no effect size of human reader improvement with AI assistance is provided.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

   Yes, standalone performance was the primary focus for most AI features mentioned, though the output was often subject to human expert review.

   • WFI: The AI network provides initialization for the RIPE algorithm. The output image quality was then reviewed by radiologists.
   • t-ACS: Performance was evaluated quantitatively against fully sampled images (reference/ground truth), indicating a standalone algorithm evaluation.
   • DeepRecon: Evaluated based on images processed by the algorithm, with expert review of the output images.
   • EasyFACT, EasyScan, EasyCrop: These are features that automate parts of the workflow. Their output (e.g., ROI placement, slice positioning, cropping) was evaluated, often subjectively by experts, but the automation itself is algorithm-driven.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

   • WFI: Expert consensus/review by three U.S. certificated radiologists for "clinical diagnosis" quality. No "ground truth" for water-fat separation accuracy itself is explicitly stated, but the problem being solved (water-fat swap artifacts) implies the improved stability of the algorithm's output.
   • t-ACS: "Fully-sampled k-space data were collected and transformed into image domain as reference." This serves as the "true" or ideal image for comparison, not derived from expert interpretation or pathology.
   • DeepRecon: "Multiple-averaged images with high-resolution and high SNR were collected as the ground-truth images." Expert review confirms diagnostic quality of processed images.
   • EasyFACT: Subjective evaluation by licensed physicians with U.S. credentials, implying their judgment regarding the correctness of ROI placement and numerical statistics.
   • EasyScan: Evaluation by a licensed MRI technologist with U.S. credentials against the "correctness" of automatic slice positioning.
   • EasyCrop: Evaluation by a licensed MRI technologist with U.S. credentials against the "correctness" of automatic cropping.

7. The sample size for the training set:

   • WFI AI module: 59 volunteers (2604 cases). Each scanned for multiple body parts and WFI protocols.
   • t-ACS AI module: Not specified as a distinct number, but "collected from a variety of anatomies, image contrasts, and acceleration factors... resulting in a large number of cases." The overall dataset for training, validation, and testing was 76 volunteers.
   • DeepRecon: 317 volunteers.
   • EasyFACT, EasyScan, EasyCrop: "The training data used for the training of the EasyFACT algorithm is independent of the data used to test the algorithm." For EasyScan and EasyCrop, it states "The testing dataset was collected independently from the training dataset," but does not provide specific training set sizes for these workflow features.

8. How the ground truth for the training set was established:

   • WFI AI module: The AI network was trained to provide accurate initialization for the RIPE algorithm. The document implies that the RIPE algorithm itself with human oversight or internal validation would have been used to establish correct water/fat separation for training.
   • t-ACS AI module: "Fully-sampled k-space data were collected and transformed into image domain as reference." This served as the ground truth against which the AI was trained to reconstruct undersampled data.
   • DeepRecon: "The multiple-averaged images with high-resolution and high SNR were collected as the ground-truth images." This indicates that high-quality, non-denoised, non-super-resolved images were used as the ideal target for the AI.
   • EasyFACT, EasyScan, EasyCrop: Not explicitly detailed beyond stating that training data ground truth was established to enable the algorithms for automatic ROI placement, slice group positioning, and image cropping, respectively. It implies a process of manually annotating or identifying the correct ROIs/positions/crops on training data for the AI to learn from.

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The uMR Ultra system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces sagittal, transverse, coronal, and oblique cross sectional images, and spectroscopic images, and that display internal anatomical structure and/or function of the head, body and extremities. These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist the diagnosis. Contrast agents may be used depending on the region of interest of the scan.

uMR Ultra is a 3T superconducting magnetic resonance diagnostic device with a 70cm size patient bore and 2 channel RF transmit system. It consists of components such as magnet, RF power amplifier, RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, and vital signal module etc. uMR Ultra is designed to conform to NEMA and DICOM standards.

Here's a breakdown of the acceptance criteria and study details for the uMR Ultra device, based on the provided FDA 510(k) clearance letter.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the document, which focuses on device clearance, multiple features are discussed. I will present the acceptance criteria and results for the AI-powered features, as these are the most relevant to the "AI performance" aspect.

Acceptance Criteria and Device Performance for AI-Enabled Features

• ACS has higher SNR than CS.
• ACS has higher (standard deviation (SD) / mean value(S)) values than CS.
• Bland-Altman analysis of image intensities acquired using fully sampled and ACS shown with less than 1% bias and all sample points falls in the 95% confidence interval.
• Measurement differences on ACS and fully sampled images of same structures under 5% is acceptable.
• Radiologists rate all ACS images with equivalent or higher scores in terms of diagnosis quality. | - Pass
• Pass
• Pass
• Pass
• Pass
• Verified that ACS meets the requirements of clinical diagnosis. All ACS images were rated with equivalent or higher scores in terms of diagnosis quality. |
  | DeepRecon | - DeepRecon images achieve higher SNR compared to NADR images.
• Uniformity difference between DeepRecon images and NADR images under 5%.
• Intensity difference between DeepRecon images and NADR images under 5%.
• Measurements on NADR and DeepRecon images of same structures, measurement difference under 5%.
• Radiologists rate all DeepRecon images with equivalent or higher scores in terms of diagnosis quality. | - NADR: 343.63, DeepRecon: 496.15 (PASS)
• 0.07% (PASS)
• 0.2% (PASS)
• 0% (PASS)
• Verified that DeepRecon meets the requirements of clinical diagnosis. All DeepRecon images were rated with equivalent or higher scores in terms of diagnosis quality. |
  | EasyScan | No Fail cases and auto position success rate P1/(P1+P2+F) exceeds 80%.
  (P1: Pass with auto positioning; P2: Pass with user adjustment; F: Fail) | 99.6% |
  | t-ACS | - AI prediction (AI module output) much closer to reference compared to AI module input images.
• Better consistency between t-ACS and reference than between CS and reference.
• No large structural difference appeared between t-ACS and reference.
• Motion-time curves and Bland-Altman analysis consistency between t-ACS and reference. | - Pass
• Pass
• Pass
• Pass |
  | AiCo | - AiCo images exhibit improved PSNR and SSIM compared to the originals.
• No significant structural differences from the gold standard.
• Radiologists confirm image quality is diagnostically acceptable, fewer motion artifacts, and greater benefits for clinical diagnosis. | - Pass
• Pass
• Confirmed. |
  | SparkCo | - Average detection accuracy needs to be > 90%.
• Average PSNR of spark-corrected images needs to be higher than spark images.
• Spark artifacts need to be reduced or corrected after enabling SparkCo. | - 94%
• 1.6 higher
• Successfully corrected |
  | ImageGuard | Success rate P/(P+F) exceeds 90%.
  (P: Pass if prompt appears for motion / no prompt for no motion; F: Fail if prompt doesn't appear for motion / prompt appears for no motion) | 100% |
  | EasyCrop | No Fail cases and pass rate P1/(P1+P2+F) exceeds 90%.
  (P1: Other peripheral tissues cropped, meets user requirements; P2: Cropped images don't meet user requirements, but can be re-cropped; F: EasyCrop fails or original images not saved) | 100% |
  | EasyFACT | Satisfied and Acceptable ratio (S+A)/(S+A+F) exceeds 95%.
  (S: All ROIs placed correctly; A: Fewer than five ROIs placed correctly; F: ROIs positioned incorrectly or none placed) | 100% |
  | Auto TI Scout | Average frame difference between auto-calculated TI and gold standard is ≤ 1 frame, and maximum frame difference is ≤ 2 frames. | Average: 0.37-0.44 frames, Maximum: 1-2 frames (PASS) |
  | Inline MOCO | Average Dice coefficient of the left ventricular myocardium after motion correction is > 0.87. | Cardiac Perfusion Images: 0.92
  Cardiac Dark Blood Images: 0.96 |
  | Inline ED/ES Phases Recognition | The average error between the phase indices calculated by the algorithm for the ED and ES of test data and the gold standard phase indices does not exceed 1 frame. | 0.13 frames |
  | Inline ECV | No failure cases, satisfaction rate S/(S+A+F) > 95%.
  (S: Segmentation adheres to myocardial boundary, blood pool ROI correct; A: Small missing/redundant areas but blood pool ROI correct; F: Myocardial mask fails or blood pool ROI incorrect) | 100% |
  | EasyRegister (Height Estimation) | PH5 (Percentage of height error within 5%); PH15 (Percentage of height error within 15%); MEAN_H (Average error of height). (Specific numerical criteria not explicitly stated beyond these metrics) | PH5: 92.4%
  PH15: 100%
  MEAN_H: 31.53mm |
  | EasyRegister (Weight Estimation) | PW10 (Percentage of weight error within 10%); PW20 (Percentage of weight error within 20%); MEAN_W (Average error of weight). (Specific numerical criteria not explicitly stated beyond these metrics) | PW10: 68.64%
  PW20: 90.68%
  MEAN_W: 6.18kg |
  | EasyBolus | No Fail cases and success rate P1+P2/(P1+P2+F) exceeds 100%.
  (P1: Monitoring point positioning meets user requirements, frame difference ≤ 1 frame; P2: Monitoring point positioning meets user requirements, frame difference = 2 frames; F: Auto position fails or frame difference > 2 frames) | P1: 80%
  P2: 20%
  Total Failure Rate: 0%
  Pass: 100% |

For the rest of the questions, I will consolidate the information where possible, as some aspects apply across multiple AI features.

2. Sample Sizes Used for the Test Set and Data Provenance

• ACS: 749 samples from 25 volunteers. Diverse demographic distributions covering various genders, age groups, ethnicity (White, Asian, Black), and BMI (Underweight, Healthy, Overweight/Obesity). Data collected from various clinical sites during separated time periods.
• DeepRecon: 25 volunteers (nearly 2200 samples). Diverse demographic distributions covering various genders, age groups, ethnicity (White, Asian, Black), and BMI. Data collected from various clinical sites during separated time periods.
• EasyScan: 444 cases from 116 subjects. Diverse demographic distributions covering various genders, age groups, and ethnicities. Data acquired from UIH MRI equipment (1.5T and 3T). Data provenance not explicitly stated (e.g., country of origin), but given the company location (China) and "U.S. credentials" for evaluators, it likely includes data from both. The document states "The testing dataset was collected independently from the training dataset".
• t-ACS: 1173 cases from 60 volunteers. Diverse demographic distributions covering various genders, age groups, ethnicities (White, Black, Asian) and BMI. Data acquired by uMR Ultra scanners. Data provenance not explicitly stated, but implies global standard testing.
• AiCo: 218 samples from 24 healthy volunteers. Diverse demographic distributions covering various genders, age groups, BMI (Under/healthy weight, Overweight/Obesity), and ethnicity (White, Black, Asian). Data provenance not explicitly stated.
• SparkCo: 59 cases from 15 patients for real-world spark raw data testing. Diverse demographic distributions including gender, age, BMI (Underweight, Healthy, Overweight, Obesity), and ethnicity (Asian, "N.A." for White, implying not tested as irrelevant). Data acquired by uMR 1.5T and uMR 3T scanners.
• ImageGuard: 191 cases from 80 subjects. Diverse demographic distributions covering various genders, age groups, and ethnicities (White, Black, Asian). Data acquired from UIH MRI equipment (1.5T and 3T).
• EasyCrop: Not explicitly stated as "subjects" vs. "cases," but tested on 5 intended imaging body parts. Sample size (N=65) implies 65 cases/scans, potentially from 65 distinct subjects or fewer if subjects had multiple scans. Diverse demographic distributions covering various genders, age groups, ethnicity (Asian, Black, White). Data acquired from UIH MRI equipment (1.5T and 3T).
• EasyFACT: 25 cases from 25 volunteers. Diverse demographic distributions covering various genders, age groups, weight, and ethnicity (Asian, White, Black).
• Auto TI Scout: 27 patients. Diverse demographic distributions covering various genders, age groups, ethnicity (Asian, White), and BMI. Data acquired from 1.5T and 3T scanners.
• Inline MOCO: Cardiac Perfusion Images: 105 cases from 60 patients. Cardiac Dark Blood Images: 182 cases from 33 patients. Diverse demographic distributions covering age, gender, ethnicity (Asian, White, Black, Hispanic), BMI, field strength, and disease conditions (Positive, Negative, Unknown).
• Inline ED/ES Phases Recognition: 95 cases from 56 volunteers, covering various genders, age groups, field strength, disease conditions (NOR, MINF, DCM, HCM, ARV), and ethnicity (Asian, White, Black).
• Inline ECV: 90 images from 28 patients. Diverse demographic distributions covering gender, age, BMI, field strength, ethnicity (Asian, White), and health status (Negative, Positive, Unknown).
• EasyRegister (Height/Weight Estimation): 118 cases from 63 patients. Diverse ethnic groups (Chinese, US, France, Germany).
• EasyBolus: 20 subjects. Diverse demographic distributions covering gender, age, field strength, and ethnicity (Asia).

Data Provenance (Retrospective/Prospective and Country of Origin):
The document states "The testing dataset was collected independently from the training dataset, with separated subjects and during different time periods." This implies a prospective collection for validation that is distinct from the training data. For ACS and DeepRecon, it explicitly mentions "US subjects" for some evaluations, but for many features, the specific country of origin for the test set is not explicitly stated beyond "diverse ethnic groups" or "Asian" which could be China (where the company is based) or other Asian populations. The use of "U.S. board-certified radiologists" and "licensed MRI technologist with U.S. credentials" for evaluation suggests the data is intended to be representative of, or directly includes, data relevant to the U.S. clinical context.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

• ACS & DeepRecon: Evaluated by "American Board of Radiologists certificated physicians" (plural, implying multiple, at least 2). Not specified how many exactly, but strong qualifications.
• EasyScan, ImageGuard, EasyCrop, EasyBolus: Evaluated by "licensed MRI technologist with U.S. credentials." For EasyBolus, it specifies "certified professionals in the United States." Number not explicitly stated beyond "the" technologist/professionals, but implying multiple for robust evaluation.
• Inline MOCO & Inline ECV: Ground truth annotations done by a "well-trained annotator" and "finally, all ground truth was evaluated by three licensed physicians with U.S. credentials." This indicates a 3-expert consensus/adjudication.
• SparkCo: "One experienced evaluator" for subjective image quality improvement.
• For other features (t-ACS, EasyFACT, Auto TI Scout, Inline ED/ES Phases Recognition, EasyRegister), the ground truth seems to be based on physical measurements (for EasyRegister) or computational metrics (for t-ACS based on fully-sampled images, and for accuracy of ROI placement against defined standards), rather than human expert adjudication for ground truth.

4. Adjudication Method (e.g., 2+1, 3+1, none) for the Test Set

• Inline MOCO & Inline ECV: "Evaluated by three licensed physicians with U.S. credentials." This implies a 3-expert consensus method for ground truth establishment.
• ACS, DeepRecon, AiCo: "Evaluated by American Board of Radiologists certificated physicians" (plural). While not explicitly stated as 2+1 or 3+1, it suggests a multi-reader review, where consensus was likely reached for the reported diagnostic quality.
• SparkCo: "One experienced evaluator" was used for subjective evaluation, implying no formal multi-reader adjudication for this specific metric.
• For features like EasyScan, ImageGuard, EasyCrop, EasyBolus (evaluated by MRI technologists) and those relying on quantitative metrics against a reference (t-ACS, EasyFACT, Auto TI Scout, EasyRegister, Inline ED/ES Phases Recognition), the "ground truth" is either defined by the system's intended function (e.g., correct auto-positioning) or a mathematically derived reference, so a traditional human adjudication method is not applicable in the same way as for diagnostic image interpretation.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

The document does not explicitly state that a formal MRMC comparative effectiveness study was performed to quantify the effect size of how much human readers improve with AI vs. without AI assistance.

Instead, the evaluations for ACS, DeepRecon, and AiCo involve "American Board of Radiologists certificated physicians" who "verified that [AI feature] meets the requirements of clinical diagnosis. All [AI feature] images were rated with equivalent or higher scores in terms of diagnosis quality." For AiCo, they confirmed images "exhibit fewer motion artifacts and offer greater benefits for clinical diagnosis." This is a qualitative assessment of diagnostic quality by experts, but not a comparative effectiveness study in the sense of measuring reader accuracy or confidence change with AI assistance.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

Yes, for many of the AI-enabled features, a standalone performance evaluation was conducted:

• ACS: Performance was evaluated by comparing quantitative metrics (NRMSE, SNR, Resolution, Contrast, Uniformity, Structure Measurement) against fully-sampled images or CS. This is a standalone evaluation.
• DeepRecon: Quantitative metrics (SNR, uniformity, contrast, structure measurement) were compared between DeepRecon and NADR (without DeepRecon) images. This is a standalone evaluation.
• t-ACS: Quantitative tests (MAE, PSNR, SSIM, structural measurements, motion-time curves) were performed comparing t-ACS and CS results against a reference. This is a standalone evaluation.
• AiCo: PSNR and SSIM values were quantitatively compared, and structural dimensions were assessed, between AiCo processed images and original/motionless reference images. This is a standalone evaluation.
• SparkCo: Spark detection accuracy was calculated, and PSNR of spark-corrected images was compared to original spark images. This is a standalone evaluation.
• Inline MOCO: Evaluated using Dice coefficient, a quantitative metric for segmentation accuracy. This is a standalone evaluation.
• Inline ED/ES Phases Recognition: Evaluated by quantifying the error between algorithm output and gold standard phase indices. This is a standalone evaluation.
• Inline ECV: Evaluated by quantitative scoring for segmentation accuracy (S, A, F criteria). This is a standalone evaluation.
• EasyRegister (Height/Weight): Evaluated by quantitative error metrics (PH5, PH15, MEAN_H; PW10, PW20, MEAN_W) against physical measurements. This is a standalone evaluation.

Features like EasyScan, ImageGuard, EasyCrop, and EasyBolus involve automated workflow assistance where the direct "diagnostic" outcome isn't solely from the algorithm, but the automated function's performance is evaluated in a standalone manner against defined success criteria.

7. The Type of Ground Truth Used

The type of ground truth varies depending on the specific AI feature:

• Reference/Fully-Sampled Data:
  • ACS, DeepRecon, t-ACS, AiCo: Fully-sampled k-space data transformed to image space served as "ground-truth" for training and as a reference for quantitative performance metrics in testing. For AiCo, "motionless data" served as gold standard.
  • SparkCo: Simulated spark artifacts generated from "spark-free raw data" provided ground truth for spark point locations in training.
• Expert Consensus/Subjective Evaluation:
  • ACS, DeepRecon, AiCo: "American Board of Radiologists certificated physicians" provided qualitative assessment of diagnostic image quality ("equivalent or higher scores," "diagnostically acceptable," "fewer motion artifacts," "greater benefits for clinical diagnosis").
  • EasyScan, ImageGuard, EasyCrop, EasyBolus: "Licensed MRI technologist with U.S. credentials" or "certified professionals in the United States" performed subjective evaluation against predefined success criteria for the workflow functionality.
  • SparkCo: One experienced evaluator for subjective image quality improvement.
• Anatomical/Physiological Measurements / Defined Standards:
  • EasyFACT: Defined criteria for ROI placement within liver parenchyma, avoiding borders/vascular structures.
  • Auto TI Scout, Inline ED/ES Phases Recognition: Gold standard phase indices were presumably established by expert review or a reference method.
  • Inline MOCO & Inline ECV: Ground truth for cardiac left ventricular myocardium segmentation was established by a "well-trained annotator" and "evaluated by three licensed physicians with U.S. credentials" (expert consensus based on anatomical boundaries).
  • EasyRegister (Height/Weight Estimation): "Precisely measured height/weight value" using "physical examination standards."

8. The Sample Size for the Training Set

• ACS: 1,262,912 samples (collected from variety of anatomies, image contrasts, and acceleration factors, scanned by UIH MRI systems).
• DeepRecon: 165,837 samples (collected from 264 volunteers, scanned by UIH MRI systems for multiple body parts and clinical protocols).
• EasyScan: Training data collection not explicitly detailed in the same way as ACS/DeepRecon (refers to "collected independently from the training dataset").
• t-ACS: Datasets collected from 108 volunteers ("large number of samples").
• AiCo: 140,000 images collected from 114 volunteers across multiple body parts and clinical protocols.
• SparkCo: 24,866 spark slices generated from 61 cases collected from 10 volunteers.
• EasyFACT, Auto TI Scout, Inline MOCO, Inline ED/ES Phases Recognition, Inline ECV, EasyRegister, EasyBolus: The document states that training data was independent of testing data but does not provide specific sample sizes for the training datasets for these features.

9. How the Ground Truth for the Training Set was Established

• ACS, DeepRecon, t-ACS, AiCo: "Fully-sampled k-space data were collected and transformed to image space as the ground-truth." For DeepRecon specifically, "multiple-averaged images with high-resolution and high SNR were collected as the ground-truth images." For AiCo, "motionless data" served as gold standard. All training data were "manually quality controlled."
• SparkCo: "The training dataset... was generated by simulating spark artifacts from spark-free raw data... with the corresponding ground truth (i.e., the location of spark points)."
• Inline MOCO & Inline ECV: The document states "all ground truth was annotated by a well-trained annotator. The annotator used an interactive tool to observe the image, and then labeled the left ventricular myocardium in the image."
• For EasyScan, EasyFACT, Auto TI Scout, Inline ED/ES Phases Recognition, EasyRegister, and EasyBolus training ground truth establishment is not explicitly detailed, only that the testing data was independent of the training data. For EasyRegister, it implies physical measurements were the basis for ground truth.

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The uMI Panvivo is a PET/CT system designed for providing anatomical and functional images. The PET provides the distribution of specific radiopharmaceuticals. CT provides diagnostic tomographic anatomical information as well as photon attenuation information for the scanned region. PET and CT scans can be performed separately. The system is intended for assessing metabolic (molecular) and physiologic functions in various parts of the body. When used with radiopharmaceuticals approved by the regulatory authority in the country of use, the uMI Panvivo system generates images depicting the distribution of these radiopharmaceuticals. The images produced by the uMI Panvivo are intended for analysis and interpretation by qualified medical professionals. They can serve as an aid in detection, localization, evaluation, diagnosis, staging, re-staging, monitoring, and/or follow-up of abnormalities, lesions, tumors, inflammation, infection, organ function, disorders, and/or diseases, in several clinical areas such as oncology, cardiology, neurology, infection and inflammation. The images produced by the system can also be used by the physician to aid in radiotherapy treatment planning and interventional radiology procedures.

The CT system can be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

The proposed device uMI Panvivo combines a 295/235 mm axial field of view (FOV) PET and 160-slice CT system to provide high quality functional and anatomical images, fast PET/CT imaging and better patient experience. The system includes PET system, CT system, patient table, power distribution unit, control and reconstruction system (host, monitor, and reconstruction computer, system software, reconstruction software), vital signal module and other accessories.

The uMI Panvivo has been previously cleared by FDA via K243538. The main modifications performed on the uMI Panvivo (K243538) in this submission are due to the addition of Deep MAC(also named AI MAC), Digital Gating(also named Self-gating), OncoFocus(also named uExcel Focus and RMC), NeuroFocus(also named HMC), DeepRecon.PET (also named as HYPER DLR or DLR), uExcel DPR (also named HYPER DPR or HYPER AiR)and uKinetics. Details about the modifications are listed as below:

• Deep MAC, Deep Learning-based Metal Artifact Correction (also named AI MAC) is an image reconstruction algorithm that combines physical beam hardening correction and deep learning technology. It is intended to correct the artifact caused by metal implants and external metal objects.

• Digital Gating (also named Self-gating, cleared via K232712) can automatically extract a respiratory motion signal from the list-mode data during acquisition which called data-driven (DD) method. The respiratory motion signal was calculated by tracking the location of center-of-distribution(COD) in body cavity mask. By using the respiratory motion signal, system can perform gate reconstruction without respiratory capture device.

• OncoFocus (also named uExcel Focus and RMC, cleared via K232712) is an AI-based algorithm to reduce respiratory motion artifacts in PET/CT images and at the same time reduce the PET/CT misalignment.

• NeuroFocus (also named HMC) is head motion correction solution, which employs a statistics-based head motion correction method that correct motion artifacts automatically using the centroid-of-distribution (COD) without manual parameter tuning to generate motion free images.

• DeepRecon.PET (also named as HYPER DLR or DLR, cleared via K193210) uses a deep learning technique to produce better SNR (signal-to-noise-ratio) image in post-processing procedure.

• uExcel DPR (also named HYPER DPR or HYPER AiR, cleared via K232712) is a deep learning-based PET reconstruction algorithm designed to enhance the SNR of reconstructed images. High-SNR images improve clinical diagnostic efficacy, particularly under low-count acquisition conditions (e.g., low-dose radiotracer administration or fast scanning protocols).

• uKinetics(cleared via K232712) is a kinetic modeling toolkit for indirect dynamic image parametric analysis and direct parametric analysis of multipass dynamic data. Image-derived input function (IDIF) can be extracted from anatomical CT images and dynamic PET images. Both IDIF and populated based input function (PBIF) can be used as input function of Patlak model to generate kinetic images which reveal biodistribution map of the metabolized molecule using indirect and direct methods.

The provided FDA 510(k) clearance letter describes the uMI Panvivo PET/CT System and mentions several new software functionalities (Deep MAC, Digital Gating, OncoFocus, NeuroFocus, DeepRecon.PET, uExcel DPR, and uKinetics). The document includes performance data for four of these functionalities: DeepRecon.PET, uExcel DPR, OncoFocus, and DeepMAC.

The following analysis focuses on the acceptance criteria and study details for these four AI-based image processing/reconstruction algorithms as detailed in the document. The document presents these as "performance verification" studies.

Overview of Acceptance Criteria and Device Performance (for DeepRecon.PET, uExcel DPR, OncoFocus, DeepMAC)

The document details the evaluation of four specific software functionalities: DeepRecon.PET, uExcel DPR, OncoFocus, and DeepMAC. Each of these has its own set of acceptance criteria and reported performance results, detailed below.

1. Table of Acceptance Criteria and Reported Device Performance

Coefficient of Variation (COV) calculated using uniform cylindrical phantom data on images reconstructed with both uExcel DPR and OSEM methods. | The averaged CR, BV, and CNR of the uExcel DPR images should be superior to those of the OSEM images.

uExcel DPR requires fewer counts to achieve a matched COV compared to OSEM. | Pass.

• NEMA IQ Phantom Analysis: an average noise reduction of 81% and an average SNR enhancement of 391% were observed.
• Uniform cylindrical Analysis: 1/10 of the counts can obtain the matching noise level. |
  | | Qualitative evaluation | uExcel DPR images reconstructed at lower counts qualitatively compared with full-count OSEM images. | uExcel DPR reconstructions with reduced count levels demonstrate comparable or superior image quality relative to higher-count OSEM reconstructions. | Pass.
• 1.72.5 MBq/kg radiopharmaceutical injection conditions, combined with 23 minutes whole-body scanning (4~6 bed positions), achieves comparable diagnostic image quality.
• Clinical evaluation by radiologists showed images sufficient for clinical diagnosis, with uExcel DPR exhibiting lower noise, better contrast, and superior sharpness compared to OSEM. |
  | OncoFocus | Volume relative to no motion correction (∆Volume). | Calculate the volume relative to no motion correction images. | The ∆Volume value is less than 0%. | Pass |
  | | Maximal standardized uptake value relative to no motion correction (∆SUVmax) | Calculate the SUVmax relative to no motion correction images. | The ∆SUVmax value is larger than 0%. | Pass |
  | DeepMAC | Quantitative evaluation | For PMMA phantom data, the average CT value in the affected area of the metal substance and the same area of the control image before and after DeepMAC was compared. | After using DeepMAC, the difference between the average CT value in the affected area of the metal substance and the same area of the control image does not exceed 10HU. | Pass |

2. Sample Sizes Used for the Test Set and Data Provenance

• DeepRecon.PET:

  • Phantoms: NEMA IQ phantoms.
  • Clinical Patients: 20 volunteers.
  • Data Provenance: "collected from various clinical sites" and explicitly stated to be "different from the training data." The document does not specify country of origin or if it's retrospective/prospective, but "volunteers were enrolled" suggests prospective collection for the test set.

• uExcel DPR:

  • Phantoms: Two NEMA IQ phantom datasets, two uniform cylindrical phantom datasets.
  • Clinical Patients: 19 human subjects.
  • Data Provenance: "derived from uMI Panvivo and uMI Panvivo S," "collected from various clinical sites and during separated time periods," and "different from the training data." "Study cohort" and "human subjects" imply prospective collection for the test set.

• OncoFocus:

  • Clinical Patients: 50 volunteers.
  • Data Provenance: "collected from general clinical scenarios" and explicitly stated to be "on cases different from the training data." "Volunteers were enrolled" suggests prospective collection for the test set.

• DeepMAC:

  • Phantoms: PMMA phantom datasets.
  • Clinical Patients: 20 human subjects.
  • Data Provenance: "from uMI Panvivo and uMI Panvivo S," "collected from various clinical sites" and explicitly stated to be "different from the training data." "Volunteers were enrolled" suggests prospective collection for the test set.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not explicitly state that experts established "ground truth" for the quantitative metrics (e.g., SUV, CNR, BV, CR, ∆Volume, ∆SUVmax, HU differences) for the test sets. These seem to be derived from physical measurements on phantoms or calculations from patient image data using established methods.

• For qualitative evaluation/clinical diagnosis assessment:

  • DeepRecon.PET: Two American Board of Radiologists certified physicians.
  • uExcel DPR: Two American board-certified nuclear medicine physicians.
  • OncoFocus: Two American Board of Radiologists-certified physicians.
  • DeepMAC: Two American Board of Radiologists certified physicians.

  The exact years of experience for these experts are not provided, only their board certification status.

4. Adjudication Method for the Test Set

The document states that the radiologists/physicians evaluated images "independently" (uExcel DPR) or simply "were evaluated by" (DeepRecon.PET, OncoFocus, DeepMAC). There is no mention of an adjudication method (such as 2+1 or 3+1 consensus) for discrepancies between reader evaluations for any of the functionalities. The evaluations appear to be separate assessments, with no stated consensus mechanism.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• The document describes qualitative evaluations by radiologists/physicians comparing the AI-processed images to conventionally processed images (OSEM/no motion correction/no MAC). These are MRMC comparative studies in the sense that multiple readers evaluated multiple cases.
• However, these studies were designed to evaluate the image quality (e.g., diagnostic sufficiency, noise, contrast, sharpness, lesion detectability, artifact reduction) of the AI-processed images compared to baseline images, rather than to measure an improvement in human reader performance (e.g., diagnostic accuracy, sensitivity, specificity, reading time) when assisted by AI vs. without AI.
• Therefore, the studies were not designed as comparative effectiveness studies measuring the effect size of human reader improvement with AI assistance. They focus on the perceived quality of the AI-processed images themselves.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, for DeepRecon.PET, uExcel DPR, OncoFocus, and DeepMAC, quantitative (phantom and numerical) evaluations were conducted that represent the standalone performance of the algorithms in terms of image metrics (e.g., SUV bias, BV, SNR, CNR, CR, COV, ∆Volume, ∆SUVmax, HU differences). These quantitative results are directly attributed to the algorithm's output without human intervention for the measurement/calculation.
• The qualitative evaluations by the physicians (described in point 3 above) also assess the output of the algorithm, but with human interpretation.

7. The Type of Ground Truth Used

• For Quantitative Evaluations:

  • Phantoms: The "ground truth" for phantom studies is implicitly the known physical properties and geometry of the NEMA IQ and PMMA phantoms, allowing for quantitative measurements (e.g., true SUV, true CR, true signal-to-noise).
  • Clinical Data (DeepRecon.PET, uExcel DPR): For these reconstruction algorithms, "ground-truth images were reconstructed from fully-sampled raw data" for the training set. For the test set, comparisons seem to be made against OSEM with Gaussian filtering or full-count OSEM images as reference/comparison points, rather than an independent "ground truth" established by an external standard.
  • Clinical Data (OncoFocus): Comparisons are made relative to "no motion correction images" (∆Volume and ∆SUVmax), implying these are the baseline for comparison, not necessarily an absolute ground truth.
  • Clinical Data (DeepMAC): Comparisons are made to a "control image" without metal artifacts for quantitative assessment of HU differences.

• For Qualitative Evaluations:

  • The "ground truth" is based on the expert consensus / qualitative assessment by the American Board-certified radiologists/nuclear medicine physicians, who compared images for attributes like noise, contrast, sharpness, motion artifact reduction, and diagnostic sufficiency. This suggests a form of expert consensus, although no specific adjudication is described. There's no mention of pathology or outcomes data as ground truth.

8. The Sample Size for the Training Set

The document provides the following for the training sets:

• DeepRecon.PET: "image samples with different tracers, covering a wide and diverse range of clinical scenarios." No specific number provided.
• uExcel DPR: "High statistical properties of the PET data acquired by the Long Axial Field-of-View (LAFOV) PET/CT system enable the model to better learn image features. Therefore, the training dataset for the AI module in the uExcel DPR system is derived from the uEXPLORER and uMI Panorama GS PET/CT systems." No specific number provided.
• OncoFocus: "The training dataset of the segmentation network (CNN-BC) and the mumap synthesis network (CNN-AC) in OncoFocus was collected from general clinical scenarios. Each subject was scanned by UIH PET/CT systems for clinical protocols. All the acquisitions ensure whole-body coverage." No specific number provided.
• DeepMAC: Not explicitly stated for the training set. Only validation dataset details are given.

9. How the Ground Truth for the Training Set Was Established

• DeepRecon.PET: "Ground-truth images were reconstructed from fully-sampled raw data. Training inputs were generated by reconstructing subsampled data at multiple down-sampling factors." This implies that the "ground truth" for training was derived from high-quality, fully-sampled (and likely high-dose) PET data.
• uExcel DPR: "Full-sampled data is used as the ground truth, while corresponding down-sampled data with varying down-sampling factors serves as the training input." Similar to DeepRecon.PET, high-quality, full-sampled data served as the ground truth.
• OncoFocus:
  • For CNN-BC (body cavity segmentation network): "The input data of CNN-BC are CT-derived attenuation coefficient maps, and the target data of the network are body cavity region images." This suggests the target (ground truth) was pre-defined body cavity regions.
  • For CNN-AC (attenuation map (umap) synthesis network): "The input data are non-attenuation-corrected (NAC) PET reconstruction images, and the target data of the network are the reference CT attenuation coefficient maps." The ground truth was "reference CT attenuation coefficient maps," likely derived from actual CT scans.
• DeepMAC: Not explicitly stated for the training set. The mention of pre-trained neural networks suggests an established training methodology, but the specific ground truth establishment is not detailed.

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The uMR 680 system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces sagittal, transverse, coronal, and oblique cross sectional images, and spectroscopic images, and that display internal anatomical structure and/or function of the head, body and extremities.

These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist the diagnosis. Contrast agents may be used depending on the region of interest of the scan.

The uMR 680 is a 1.5T superconducting magnetic resonance diagnostic device with a 70cm size patient bore. It consists of components such as magnet, RF power amplifier, RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, and vital signal module etc. The uMR 680 Magnetic Resonance Diagnostic Device is designed to conform to NEMA and DICOM standards.

This traditional 510(k) is to request modifications for the cleared uMR 680(K240744). The modifications performed on the uMR 680 in this submission are due to the following changes that include:
(1) Addition of RF coils and corresponding accessories: Breast Coil -12, Biopsy Configuration, Head Coil-16, Positioning Couch-top, Coil Support.
(2) Deletion of VSM (Wireless UIH Gating Unit REF 453564324621, ECG module Ref 989803163121, SpO2 module Ref 989803163111).
(3) Modification of the dimensions of Detachable table: from width 826mm, height 880mm,2578mm to width 810mm, height 880mm, length 2505mm.
(4) Addition and modification of pulse sequences
a) New sequences: gre_snap, gre_quick_4dncemra, gre_pass, gre_mtp, gre_trass, epi_dwi_msh, epi_dti_msh, svs_hise.
b) Added associated options for certain sequences: fse(add Silicone-Only Imaging, MicroView, MTC, MultiBand), fse_arms(add Silicone-Only Imaging), fse_ssh(add Silicone-Only Imaging), fse_mx(add CEST, T1rho, MicroView, MTC), fse_arms_dwi(add MultiBand), asl_3d(add multi-PLD), gre(add T1rho, MTC, output phase image), gre_fsp(add FSP+), gre_bssfp(add CASS, TI Scout), gre_fsp_c(add 3D LGE, DB/GB PSIR), gre_bssfp_ucs(add real time cine), gre_fq(add 4D Flow), epi_dwi(add IVIM), epi_dti(add DKI, DSI).
c) Added additional accessory equipment required for certain sequences: gre_bssfp(add Virtual ECG Trigger).
d) Name change of certain sequences: gre_fine(old name: gre_bssfp_fi).
e) Added applicable body parts: gre_ute, gre_fine, fse_mx.
(5) Addition of imaging reconstruction methods: AI-assisted Compressed Sensing (ACS), Spark artifact Correction (SparkCo).
(6) Addition of imaging processing methods: Inline Cardiac Function, Inline ECV, Inline MRS, Inline MOCO, 4D Flow, SNAP, CEST, T1rho, FSP+, CASS, PASS, MTP.
(7) Addition of workflow features: TI Scout, EasyCrop, ImageGuard, Mocap, EasyFACT, Auto Bolus tracker, Breast Biopsy and uVision.
(8) Modification of workflow features: EasyScan(add applicable body parts)

The modification does not affect the intended use or alter the fundamental scientific technology of the device.

The provided FDA 510(k) clearance letter and summary for the uMR 680 Magnetic Resonance Imaging System outlines performance data for several new features and algorithms.

Here's an analysis of the acceptance criteria and the studies that prove the device meets them for the AI-assisted Compressed Sensing (ACS), SparkCo, Inline ED/ES Phases Recognition, and Inline MOCO algorithms.

1. Table of Acceptance Criteria and Reported Device Performance

• Age: 0.92-0.93
• Gender: 0.92
• Ethnicity: 0.91-0.92
• BMI: 0.91-0.95
• Magnetic field strength: 0.92-0.93
• Disease conditions: 0.91-0.93 |
  | | Dice Coefficient (Left Ventricular Myocardium after Motion Correction) Cardiac Dark Blood Images | The average Dice coefficient of the left ventricular myocardium after motion correction is greater than 0.87. | The average Dice coefficient of the left ventricular myocardium after motion correction is 0.96, which is greater than 0.87. Subgroup analysis also showed good generalization:
• Age: 0.95-0.96
• Gender: 0.96
• Ethnicity: 0.95-0.96
• BMI: 0.96-0.98
• Magnetic field strength: 0.96
• Disease conditions: 0.96-0.97 |

2. Sample Size Used for the Test Set and Data Provenance

• AI-assisted Compressed Sensing (ACS):
  • Sample Size: 1724 samples from 35 volunteers.
  • Data Provenance: Diverse demographic distributions (gender, age groups, ethnicity, BMI) covering various clinical sites and separated time periods. Implied to be prospective or a carefully curated retrospective set, collected specifically for validation on the uMR 680 system, and independent of training data.
• SparkCo:
  • Simulated Spark Testing Dataset: 159 spark slices (generated from spark-free raw data).
  • Real-world Spark Testing Dataset: 59 cases from 15 patients.
  • Data Provenance: Real-world data acquired from uMR 1.5T and uMR 3T scanners, covering representative clinical protocols. The report specifies "Asian" for 100% of the real-world dataset's ethnicity, noting that performance is "irrelevant with human ethnicity" due to the nature of spark signal detection. This is retrospective data.
• Inline ED/ES Phases Recognition:
  • Sample Size: 95 cases from 56 volunteers.
  • Data Provenance: Includes various ages, genders, field strengths (1.5T, 3.0T), disease conditions (NOR, MINF, DCM, HCM, ARV), and ethnicities (Asian, White, Black). The data is independent of the training data. Implied to be retrospective from UIH MRI systems.
• Inline MOCO:
  • Sample Size: 287 cases in total (105 cardiac perfusion images from 60 patients, 182 cardiac dark blood images from 33 patients).
  • Data Provenance: Acquired from 1.5T and 3T magnetic resonance imaging equipment from UIH. Covers various ages, genders, ethnicities (Asian, White, Black, Hispanic), BMI, field strengths (1.5T, 3.0T), and disease conditions (Positive, Negative, Unknown). The data is independent of the training data. Implied to be retrospective from UIH MRI systems.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and the Qualifications of Those Experts

• AI-assisted Compressed Sensing (ACS):
  • Number of Experts: More than one (plural "radiologists" used).
  • Qualifications: American Board of Radiologists certificated physicians.
• SparkCo:
  • Number of Experts: One expert for real-world SparkCo evaluation.
  • Qualifications: "one experienced evaluator." (Specific qualifications like board certification or years of experience are not provided for this specific evaluator).
• Inline ED/ES Phases Recognition:
  • Number of Experts: Not explicitly stated for ground truth establishment ("gold standard phase indices"). It implies a single, established method or perhaps a consensus by a team, but details are missing.
• Inline MOCO:
  • Number of Experts: Three licensed physicians.
  • Qualifications: U.S. credentials.

4. Adjudication Method for the Test Set

• AI-assisted Compressed Sensing (ACS): Not explicitly stated, but implies individual review by "radiologists" to rate diagnostic quality.
• SparkCo: For the real-world dataset, evaluation by "one experienced evaluator."
• Inline ED/ES Phases Recognition: Not explicitly stated; "gold standard phase indices" are referenced, implying a pre-defined or established method without detailing a multi-reader adjudication process.
• Inline MOCO: "Finally, all ground truth was evaluated by three licensed physicians with U.S. credentials." This suggests an adjudication or confirmation process, but the specific method (e.g., 2+1, consensus) is not detailed beyond "evaluated by."

5. If a Multi-Reader, Multi-Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

• No MRMC comparative effectiveness study was explicitly described to evaluate human reader improvement with AI assistance. The described studies focus on the standalone performance of the algorithms or a qualitative assessment of images by radiologists for diagnostic quality.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, standalone performance was done for all listed algorithms.
  • ACS: Evaluated quantitatively (SNR, Resolution, Contrast, Uniformity, Structure Measurement) and then qualitatively by radiologists. The quantitative metrics are standalone.
  • SparkCo: Quantitative metrics (Detection Accuracy, PSNR) and qualitative assessment by an experienced evaluator. The quantitative metrics are standalone.
  • Inline ED/ES Phases Recognition: Evaluated quantitatively as the error between algorithmic output and gold standard. This is a standalone performance metric.
  • Inline MOCO: Evaluated using the Dice coefficient, which is a standalone quantitative metric comparing algorithm output to ground truth.

7. The Type of Ground Truth Used

• AI-assisted Compressed Sensing (ACS):
  • Quantitative: Fully-sampled k-space data transformed to image space.
  • Clinical: Radiologist evaluation ("American Board of Radiologists certificated physicians").
• SparkCo:
  • Spark Detection Module: Location of spark points (ground truth for simulated data).
  • Spark Correction Module: Visual assessment by "one experienced evaluator."
• Inline ED/ES Phases Recognition: "Gold standard phase indices" (method for establishing this gold standard is not detailed, but implies expert-derived or a highly accurate reference).
• Inline MOCO: Left ventricular myocardium segmentation annotated by a "well-trained annotator" and "evaluated by three licensed physicians with U.S. credentials." This is an expert consensus/pathology-like ground truth.

8. The Sample Size for the Training Set

• AI-assisted Compressed Sensing (ACS): 1,262,912 samples (from a variety of anatomies, image contrasts, and acceleration factors).
• SparkCo: 24,866 spark slices (generated from 61 spark-free cases from 10 volunteers).
• Inline ED/ES Phases Recognition: Not explicitly provided, but stated to be "independent of the data used to test the algorithm."
• Inline MOCO: Not explicitly provided, but stated to be "independent of the data used to test the algorithm."

9. How the Ground Truth for the Training Set Was Established

• AI-assisted Compressed Sensing (ACS): Fully-sampled k-space data were collected and transformed to image space as the ground-truth. All data were manually quality controlled.
• SparkCo: "The training dataset for the AI module in SparkCo was generated by simulating spark artifacts from spark-free raw data... a total of 24,866 spark slices, along with the corresponding ground truth (i.e., the location of spark points), were generated for training." This indicates a hybrid approach using real spark-free data to simulate and generate the ground truth for spark locations.
• Inline ED/ES Phases Recognition: Not explicitly provided.
• Inline MOCO: Not explicitly provided.

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uOmniscan is a software application for providing real-time communication between remote and local users, providing remote read-only or fully controlled access to connected medical imaging devices, including the ability to remotely initiate MR scans. It is also used for training medical personnel in the use of medical imaging devices. It is a vendor-neutral solution. Access must be authorized by the onsite user operating the system. Images reviewed remotely are not for diagnostic use.

uOmniscan is a medical software designed to address the skill differences among technicians and their need for immediate support, allowing them to interact directly with remote experts connected to the hospital network. By collaboration between on-site technicians and remote experts, it enables technicians or radiologists located in different geographic areas to remotely assist in operating medical imaging devices. uOmniscan provides healthcare professionals with a private, secure communication platform for real-time image viewing and collaboration across multiple sites and organizations.

uOmniscan establishes remote connections with Modality through application, KVM (Keyboard, Video, Mouse) switch, or UIH's proprietary access tool uRemote Assistant. The connection can be made in full control or read-only mode, assisting on-site technicians in seeking guidance and real-time support on scan-related issues, including but not limited to training, protocol evaluation, and scan parameter management, with the capability to remotely initiate scans for MR imaging equipment. In addition to supporting remote access and control of modality scanners, uOmniscan also supports common communication methods including real-time video, as well as audio calls and text chats between users.

Images viewed remotely are not for diagnostic purposes.

It is a vendor-neutral solution compatible with existing multimodality equipment in healthcare networks, allowing healthcare professionals to share expertise and increase work efficiency, while enhancing the communication capabilities among healthcare professionals at different locations.

The provided FDA 510(k) clearance letter for the uOmniscan device focuses primarily on demonstrating substantial equivalence to a predicate device, as opposed to proving novel clinical efficacy or diagnostic accuracy. Therefore, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this context are related to performance verification and usability testing to ensure the device performs its intended functions safely and effectively, similar to the predicate device.

The document states that "No clinical study was required," and "No animal study was required." This indicates that the device's function (remote control and communication for medical imaging) does not require a traditional clinical outcomes study in the same way a diagnostic AI algorithm might. Therefore, the "study that proves the device meets the acceptance criteria" refers to the software verification and validation testing, performance verification, and usability studies conducted.

Here's a breakdown of the requested information based on the provided text:

Acceptance Criteria and Device Performance

The acceptance criteria for this type of device are primarily functional and usability-based, ensuring it performs its tasks reliably and is safe for user interaction.

Study Details

Given the nature of the device and the FDA's clearance pathway, the "study" referred to is a series of engineering and usability tests rather than a clinical trial.

2. Sample size used for the test set and the data provenance

• Test Set Sample Size: The document does not specify quantitative sample sizes for the functional performance or usability testing datasets (e.g., number of communication sessions tested, specific network conditions, or number of users in usability testing). It broadly states that "Evaluation testing was conducted to verify the functions" and "Expert review for formative evaluation and usability testing for summative evaluation were conducted."
• Data Provenance: Not explicitly stated, but given the manufacturer is "Shanghai United Imaging Healthcare Co., Ltd." in China, it's reasonable to infer that the testing likely occurred in a controlled environment by the manufacturer or their designated testing facilities, potentially in China or other regions where their systems are developed/used. The tests conducted (software V&V, performance verification, usability) are typically internal, controlled studies rather than real-world data collection. The data would be prospective in the sense that the tests were designed and executed to evaluate the device.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Ground Truth Establishment: For functional and usability testing of a remote control/communication software, "ground truth" isn't established in the clinical sense (e.g., disease presence). Instead, the "truth" is whether the software correctly performs its programmed functions and is usable.
• Experts: The usability study involved "participation of experts and user representatives." The specific number or detailed qualifications of these "experts" (e.g., 'radiologist with 10 years experience') are not specified in this summary. They would likely be human factors engineers, software testers, and potentially medical professionals (radiologists, technologists) acting as user representatives.

4. Adjudication method for the test set

• Adjudication Method: Not applicable or specified in the traditional sense of medical image interpretation (e.g., 2+1 radiology review). For software functional testing, results are typically binary (pass/fail) based on predefined test cases. For usability, a consensus or qualitative analysis of user feedback and observations would be used, but a formal "adjudication method" as seen in clinical reading studies is not mentioned.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No, a multi-reader, multi-case (MRMC) comparative effectiveness study was not done. The document explicitly states: "No clinical study was required." This type of study is typically performed for AI or CAD devices that assist with diagnostic interpretation, which is not the primary function of uOmniscan. The device is for remote control and communication, and "Images reviewed remotely are not for diagnostic use."

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Standalone Performance: The "Performance Verification" section details tests of the software's functional capabilities (e.g., establishing communication, network status). These could be considered "standalone" in the sense that they verify the software's inherent ability to perform these tasks. However, the device's core purpose is "real-time communication between remote and local users" and "access to connected medical imaging devices," implying a human-in-the-loop for its intended use. The testing confirms the software's readiness for this human-in-the-loop interaction rather than a pure standalone diagnostic performance.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: As noted, traditional "ground truth" (e.g., pathology, clinical outcomes, expert consensus on disease) is not applicable for this device's verification. Instead, the ground truth for performance testing is the predefined functional requirements and expected system behavior, as well as the principles of human factors engineering and usability standards for the usability study.

8. The sample size for the training set

• Training Set Sample Size: Not applicable/not specified. The uOmniscan device is described as "software only solution" for remote control and communication. There is no mention of it being an AI/ML algorithm that requires a "training set" in the context of machine learning model development. The verification and validation data are for testing the implemented software features, not for training a model.

9. How the ground truth for the training set was established

• Ground Truth for Training Set Establishment: Not applicable. As the device does not appear to be an AI/ML system requiring a training set, the concept of establishing ground truth for a training set does not apply here.

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The uMR Omega system is indicated for use as a magnetic resonance diagnostic device (MRDD) that produces sagittal, transverse, coronal, and oblique cross sectional images, and spectroscopic images, and that display internal anatomical structure and/or function of the head, body and extremities.

These images and the physical parameters derived from the images when interpreted by a trained physician yield information that may assist the diagnosis. Contrast agents may be used depending on the region of interest of the scan.

The uMR Omega is a 3.0T superconducting magnetic resonance diagnostic device with a 75cm size patient bore. It consists of components such as magnet, RF power amplifier, RF coils, gradient power amplifier, gradient coils, patient table, spectrometer, computer, equipment cabinets, power distribution system, internal communication system, and vital signal module etc. The uMR Omega Magnetic Resonance Diagnostic Device is designed to conform to NEMA and DICOM standards.

This traditional 510(k) is to request modifications for the cleared uMR Omega(K240540). The modifications performed on the uMR Omega in this submission are due to the following changes that include:

1. Addition of RF coils and corresponding accessories: Breast Coil - 12, Biopsy Configuration, Head Coil - 16, Positioning Couch-top, Coil Support, Tx/Rx Head Coil.

2. Modification of the mmw component name: from mmw100 to mmw101.

3. Modification of the dimensions of detachable table: from width 826mm, height 880mm, length 2578mm to width 810mm, height 880mm, length 2505mm.

4. Addition and modification of pulse sequences:

   • a) New sequences: gre_pass, gre_mtp, epi_dti_msh, gre_fsp_c(3D LGE).

   • b) Added Associated options for certain sequences: fse(MicroView), fse_mx(MicroView), gre(Output phase image), gre_swi(QSM),
     gre_fsp_c(DB/GB PSIR), gre_bssfp(TI Scout), gre_bssfp_ucs(Real Time Cine), epi_dwi(IVIM), epi_dti(DSI, DKI).

   • c) Added Additional accessory equipment required for certain sequences: gre_bssfp (Virtual ECG Trigger).

   • d) Added applicable body parts: epi_dwi_msh, gre_fine, fse_mx.

5. Addition of imaging processing methods: Inline Cardiac function, Inline ECV, Inline MRS, Inline MOCO and MTP.

6. Addition of workflow features: EasyFACT, TI Scout, EasyCrop, ImageGuard, MoCap and Breast Biopsy.

7. Addition of image reconstruction methods: SparkCo.

8. Modification of function: uVision (add Body Part Recognization), EasyScan(add applicable body parts).

The modification does not affect the intended use or alter the fundamental scientific technology of the device.

The provided text describes modifications to an existing MR diagnostic device (uMR Omega) and performs non-clinical testing to demonstrate substantial equivalence to predicate devices. It specifically details the acceptance criteria and study results for two components: SparkCo (an AI algorithm for spark artifact correction) and Inline ECV (an image processing method for extracellular volume fraction calculation).

Here's a breakdown of the requested information:

Acceptance Criteria and Device Performance for uMR Omega

1. Table of Acceptance Criteria and Reported Device Performance

For SparkCo (Spark artifact Correction):

2. Based on the real-world spark dataset, evaluating the image quality improvement between the spark-corrected images and spark images by one experienced evaluator. | 1. The average PSNR of spark-corrected images needs to be higher than the spark images.
3. Spark artifacts need to be reduced or corrected after enabling SparkCo. | 1. The average PSNR of spark-corrected images is 1.6 higher than the spark images.
4. The images with spark artifacts were successfully corrected after enabling the SparkCo. |

For Inline ECV (Extracellular Volume Fraction):

The criteria is as follows:
• Satisfied (S): the segmentation myocardial boundary adheres to the myocardial boundary and blood pool ROI is within the blood pool excluding the papillary muscles.
• Acceptable (A): These are small missing or redundant areas in the myocardial segmentation but not obviously and the blood pool ROI is within the blood pool excluding the papillary muscles.
• Fail (F): The myocardial mask does not adhere to the myocardial boundary or the blood pool ROI is not within the blood pool, or the blood pool ROI contains papillary muscles. | The segmentation algorithm performed as expected in different subgroups.
Total satisfaction Rate (S): 100% for all monitored demographic and acquisition subgroups, which means no failure cases (F) or acceptable cases (A) were reported. |

2. Sample Size Used for the Test Set and Data Provenance

For SparkCo:

• Test Set Sample Size:
  • Simulated Spark Testing Dataset: 159 spark slices.
  • Real-world Spark Testing Dataset: 59 cases from 15 patients.
• Data Provenance:
  • Simulated Spark Testing Dataset: Generated by simulating spark artifacts from spark-free raw data (61 cases from 10 volunteers, various body parts and MRI sequences).
  • Real-world Spark Testing Dataset: Acquired using uMR 1.5T and uMR 3T scanners, covering representative clinical protocols (T1, T2, PD with/without fat saturation) from 15 patients. The ethnicity for this dataset is 100% Asian, and the data originates from an unspecified location, but given the manufacturer's location (Shanghai, China), it is highly likely to be China. This appears to be retrospective as patients data is mentioned.

For Inline ECV:

• Test Set Sample Size: 90 images from 28 patients.
• Data Provenance: The distribution table shows data from patients with magnetic field strengths of 1.5T and 3T. Ethn_icity is broken down into "Asia" (17 patients) and "USA" (11 patients). This indicates a combined dataset potentially from multiple geographical locations, and appears to be retrospective.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

For SparkCo:

• Spark detection accuracy: The ground truth for spark detection accuracy was established by comparing to "ground-truth" spark locations, which were generated as part of the simulation process for the training data and likely also for evaluating the testing set during the simulation step. For the real-world dataset, the document mentions "comparing the spark detection results with the ground-truth" implying an existing ground truth, but doesn't specify how it was established or how many experts were involved.
• Spark correction performance: "One experienced evaluator" was used for subjective evaluation of image quality improvement on the real-world spark dataset. No specific qualifications are provided for this evaluator beyond "experienced".

For Inline ECV:

• The document states, "The segmentation result of each case was obtained with the algorithm, and the segmentation mask was evaluated with the following criteria." It does not explicitly mention human experts establishing a distinct "ground truth" for each segmentation mask for the purpose of the acceptance criteria. Instead, the evaluation seems to be a subjective assessment against predefined criteria. No number of experts or qualifications are provided.

4. Adjudication Method for the Test Set

For SparkCo:

• For spark detection accuracy, the comparison was against a presumed inherent "ground-truth" (likely derived from the simulation process).
• For spark correction performance, a single "experienced evaluator" made the subjective assessment, implying no adjudication method (e.g., 2+1, 3+1) was explicitly used among multiple experts.

For Inline ECV:

• The evaluation was a "subjective evaluation method" against specific criteria. No information about multiple evaluators or an adjudication method is provided. It implies a single evaluator or an internal consensus without formal adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not explicitly mentioned for either SparkCo or Inline ECV. The studies were focused on the standalone performance of the algorithms.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done

• Yes, for both SparkCo and Inline ECV, the studies described are standalone algorithm performance evaluations.
  • SparkCo focused on the algorithm's ability to detect and correct spark artifacts (objective metrics like PSNR and subjective assessment by one evaluator).
  • Inline ECV focused on the algorithm's segmentation accuracy (subjective evaluation of segmentation masks against criteria).

7. The Type of Ground Truth Used

For SparkCo:

• Spark detection accuracy: Ground truth was generated by simulating spark artifacts from spark-free raw data, implying a simulated/synthetic ground truth for training and a comparison against this for testing. For real-world data, the "ground-truth" for detection is implied but not explicitly detailed how it was established.
• Spark correction performance: For PSNR, the "ground truth" for comparison is the original spark images. For subjective evaluation, it's against the "spark images" and the expectation of correction, suggesting human expert judgment (by one evaluator) rather than a pre-established clinical ground truth for each case.

For Inline ECV:

• The ground truth for Inline ECV appears to be a subjective expert assessment (though the number of experts is not specified) of the algorithm's automatically generated segmentation masks against predefined "Satisfied," "Acceptable," and "Fail" criteria. It is not an independent, pre-established ground truth like pathology or outcomes data.

8. The Sample Size for the Training Set

For SparkCo:

• Training dataset for the AI module: 61 cases from 10 volunteers. From this, a total of 24,866 spark slices along with corresponding "ground truth" (location of spark points) were generated for training.

For Inline ECV:

• The document states, "The training data used for the training of the cardiac ventricular segmentation algorithm is independent of the data used to test the algorithm." However, the sample size for the training set itself is not explicitly provided in the given text.

9. How the Ground Truth for the Training Set Was Established

For SparkCo:

• The ground truth for the SparkCo training set was established by simulating spark artifacts from spark-free raw data. This simulation process directly provided the "location of spark points" as the ground truth.

For Inline ECV:

• The document mentions that the training data is independent of the test data, but it does not describe how the ground truth for the training set of the Inline ECV algorithm was established.

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uCT ATLAS Astound is a computed tomography x-ray system, which is intended to produce cross-sectional images of the whole body by computer reconstruction of x-ray transmission data taken at different angles and planes. uCT ATLAS Astound is applicable to head, whole body, cardiac, and vascular x-ray Computed Tomography.

uCT ATLAS Astound is intended to be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

*Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

uCT ATLAS is a computed tomography x-ray system, which is intended to produce cross-sectional images of the whole body by computer reconstruction of x-ray transmission data taken at different angles and planes. uCT ATLAS is applicable to head, whole body, cardiac, and vascular x-ray Computed Tomography.

uCT ATLAS has the capability to image a whole organ in a single rotation. Organs include, but not limited to head, heart, liver, kidney, pancreas, joints, etc.

uCT ATLAS is intended to be used for low dose CT lung cancer screening for the early detection of lung nodules that may represent cancer. The screening must be performed within the established inclusion criteria of programs / protocols that have been approved and published by either a governmental body or professional medical society.

*Please refer to clinical literature, including the results of the National Lung Screening Trial (N Engl J Med 2011; 365:395-409) and subsequent literature, for further information.

uWS-CT-Dual Energy Analysis is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The various materials of an anatomical region of interest have different attenuation coefficients, which depend on the used energy. These differences provide information on the chemical composition of the scanned body materials and enable images to be generated at multiple energies within the available spectrum. uWS-CT-Dual Energy Analysis software combines images acquired with low and high energy spectra to visualize this information.

The uCT ATLAS Astound with uWS-CT Dual Energy Analysis and uCT ATLAS with uWS-CT Dual Energy Analysis includes the same intended use and same indications for use as their recent cleared versions (K231482). The reason for this submission is to support the following additional functions:

• CardioXphase (optimized)
• CardioBoost
• CardioCapture (optimized)
• AIIR
• Motion Freeze
• Ultra EFOV

The provided text describes a 510(k) premarket notification for a Computed Tomography X-ray System (uCT ATLAS Astound with uWS-CT-Dual Energy Analysis and uCT ATLAS with uWS-CT-Dual Energy Analysis). The submission focuses on additional software functions beyond what was previously cleared.

However, the document does not contain specific acceptance criteria, detailed study designs, or quantitative performance data to establish "proof" in the typical sense of a rigorous clinical trial with defined endpoints and statistical significance. Instead, it relies on demonstrating substantial equivalence to existing predicate devices.

The "acceptance criteria" appear to be implicit in the non-clinical and reader studies, aiming to show that the performance of the new features is "sufficient for diagnosis," "equal or better," or "can improve" compared to a baseline or predicate. No explicit numerical thresholds for metrics like sensitivity, specificity, accuracy, or effect sizes for reader improvement are provided.

Here's an analysis based on the information provided, highlighting what is present and what is missing concerning acceptance criteria and study details:

Overview of Device Performance and Acceptance Criteria

The submission does not explicitly define acceptance criteria in terms of numerical thresholds for performance metrics. Instead, it describes a "bench test" and "reader study" approach to demonstrate that the new functions do not raise new safety and effectiveness concerns and provide an equivalent or improved performance compared to the predicate/reference devices or established techniques.

The implied "acceptance criteria" are qualitative, such as:

• "passed the basic general IQ test which satisfied the requirement of IEC 61223-3-5."
• "showed better LCD comparing with FBP..."
• "showed better noise comparing with FBP."
• "showed better spatial resolution comparing with FBP..."
• "all indicators have met the verification criteria and have passed the verification." (for CardioXphase)
• "can reduce head motion artifacts." (for Motion Freeze)
• "can improve the CT number..." (for Ultra EFOV)
• "images are sufficient for diagnosis and the image quality... is equal or better than..." (for various reader studies)
• "is helpful for both artifact suppression and clinical diagnosis." (for Motion Freeze reader study)
• "can improve the accuracy of image CT numbers..." (for Ultra EFOV reader study)
• "conclude the effectiveness of CardioCapture function for reducing cardiac motion artifacts as expected."

Table of Acceptance Criteria (Implied) and Reported Device Performance

Since explicit, quantitative acceptance criteria are not provided, this table will rephrase the reported performance as the observed outcome against the implied objective.

Study Details

1. Sample Size Used for the Test Set and Data Provenance:

   • Test Set Sample Size: The document does not specify the sample sizes (number of cases/studies) used for either the bench tests or the reader studies.
   • Data Provenance: Not explicitly stated (e.g., country of origin, whether retrospective or prospective). The use of "clinical images" implies real patient data, but details are missing.

2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

   • Number of Experts: Not specified. The document mentions "readers" (plural) for the reader studies but does not state how many participated.
   • Qualifications of Experts: Not specified. No details are given about their specialty (e.g., cardiologist, radiologist), experience level, or board certification.

3. Adjudication Method for the Test Set:

   • Adjudication Method: Not specified. For the reader studies, it only states that images "were shown to the readers to perform a five-point scale evaluation" or "5-point scale evaluation." There's no mention of how discrepancies or disagreements among readers were handled or if a consensus ground truth was established by independent experts (e.g., 2+1, 3+1).

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done, What was the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance:

   • MRMC Study: Reader studies were conducted comparing images reconstructed with the new AI functions (e.g., CardioBoost) to those reconstructed with traditional methods (e.g., KARL 3D, FBP). These appear to be MRMC studies in structure, as multiple readers evaluate multiple cases.
   • Effect Size: No quantitative effect sizes are provided. The results are qualitative: "equal or better," "sufficient for diagnosis," "helpful." There are no reported metrics like AUC improvement, sensitivity/specificity gains, or statistical significance of differences in reader performance with and without AI assistance.

5. If a Standalone (i.e., Algorithm Only Without Human-in-the-Loop Performance) Was Done:

   • Standalone Performance: The "bench tests" for CardioBoost, AIIR, Motion Freeze, and Ultra EFOV evaluate the algorithms' image quality metrics (IQ, LCD, noise, spatial resolution, CT value accuracy, artifact reduction) independently of human interpretation. For CardioXphase, the evaluation of the AI module's extraction accuracy (DICE, Precision, Recall) is also a standalone assessment. These can be considered standalone performance evaluations for the image reconstruction/processing algorithms.

6. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.):

   • Ground Truth: For the image quality bench tests (CardioBoost, AIIR, Motion Freeze, Ultra EFOV), the "ground truth" is likely defined by physical phantom measurements and adherence to engineering specifications/standards (e.g., IEC 61223-3-5, CTIQ White Paper, AAPM's report).
   • For CardioXphase, the ground truth for image segmentation accuracy (heart and coronary artery masks) was "annotated results," which typically implies expert manual annotation on imaging data.
   • For the reader studies, the "ground truth" is based on the subjective evaluation of "image quality aspects" by the readers, rather than an objective, clinically validated ground truth for a diagnostic endpoint (e.g., presence/absence of disease confirmed by biopsy or follow-up). The goal was to demonstrate that the image quality generated by the new features is non-inferior or improved for diagnostic purposes.

7. The Sample Size for the Training Set:

   • Training Set Sample Size: The document mentions "datasets augmentation and deep learning network optimization" for CardioBoost and AIIR, and "introduction of a new deep learning based coronaries detection algorithm" for CardioXphase, and "introduces a deep learning network" for Ultra EFOV. However, the specific size of the training datasets (number of images/cases) is not provided.

8. How the Ground Truth for the Training Set Was Established:

   • Training Set Ground Truth: Not explicitly stated. For deep learning models, training data ground truth is typically established by expert annotation or labels derived from existing clinical reports or imaging features. Given the context of image reconstruction and enhancement, it likely involves high-quality, potentially expert-annotated, imaging data. For instance, for CardioXphase, the ground truth for training the coronary artery detection algorithm would involve expert-labeled coronary anatomy. For features like CardioBoost and AIIR, which optimize image reconstruction, the ground truth for training might involve pairs of raw data and ideal reconstructed images, or image quality metrics derived from expert evaluations on initial datasets.

In summary, the 510(k) submission successfully demonstrates "substantial equivalence" based on qualitative assessments and performance relative to known methods. However, for a detailed "proof" with explicit acceptance criteria and quantitative performance metrics, further information beyond what is presented in this FDA clearance letter summary would be needed. This is characteristic of many 510(k) submissions, which often rely on demonstrating safety and effectiveness relative to existing predicates rather than establishing novel clinical efficacy through large-scale, quantitatively defined trials.

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uOmnispace.CT is a software for viewing, manipulating, evaluating and analyzing medical images. It supports interpretation and evaluation of examinations within healthcare institutions. It has the following additional indications:

• The uOmnispace.CT Colon Analysis application is intended to provide the user a tool to enable easy visualization and efficient evaluation of CT volume data sets of the colon.
• The uOmnispace.CT Dental Application is intended to provide the user a tool to reconstruct panoramic and paraxial views of jaw.
• The uOmnispace.CT Lung Density Analysis application is intended to segment pulmonary, lobes, and airway, providing the user quantitative parameters, structure information to evaluate the lung and airway.
• The uOmnispace.CT Lung Nodule application is intended to provide the user a tool for the review and analysis of thoracic CT images, providing quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies.
• The uOmnispace.CT Vessel Analysis application is intended to provide a tool for viewing, manipulating, and evaluating CT vascular images.
• The uOmnispace.CT Brain Perfusion application is intended to calculate the parameters such as: CBV, CBF, etc. in order to analyze functional blood flow information about a region of interest (ROI) in brain.
• The uOmnispace.CT Heart application is intended to segment heart and extract coronary artery. It also provides analysis of vascular stenosis, plaque and heart function.
• The uOmnispace.CT Calcium Scoring application is intended to identify calcifications and calculate the calcium score.
• The uOmnispace.CT Dynamic Analysis application is intended to support visualization of the CT datasets over time with the 3D/4D display modes.
• The uOmnispace.CT Bone Structure Analysis application is intended to provide visualization and labels for the ribs and spine, and support batch function for intervertebral disk.
• The uOmnispace.CT Liver Evaluation application is intended to provide processing and visualization for liver segmentation and vessel extraction. It also provides a tool for the user to perform liver separation and residual liver segments evaluation.
• The uOmnispace.CT Dual Energy is a post-processing software package that accepts UIH CT images acquired using different tube voltages and/or tube currents of the same anatomical location. The Dual Energy application is intended to provide information on the chemical composition of the scanned body materials and/or contrast agents. Additionally, it enables images to be generated at multiple energies within the available spectrum.
• The uOmnispace.CT Cardiovascular Combined Analysis is an image analysis software package for evaluating contrast enhanced CT images. The CT Cardiovascular Combined Analysis is intended to analyze vascular and cardiac structures.It can be used in the qualitative and quantitative for the analysis of head-neck, abdomen, multi-body part combined, TAVR (Transcatheter Aortic Valve Replacement) CT data as input for the planning of cardiovascular procedures.
• The uOmnispace.CT Body Perfusion is intended to analyze blood flow information of dynamic CT images, by providing various perfusion-related parameters of the body parts.

The uOmnispace.CT is a post-processing software based on the uOmnispace platform for viewing, manipulating, evaluating and analyzing medical images, can run alone or with other advanced commercially cleared applications.

uOmnispace.CT contains the following applications:

• uOmnispace.CT Calcium Scoring
• uOmnispace.CT Lung Nodule
• uOmnispace.CT Colon Analysis
• uOmnispace.CT Lung Density Analysis
• uOmnispace.CT Dental Application
• uOmnispace.CT Bone Structure Analysis
• uOmnispace.CT Dual Energy
• uOmnispace.CT Vessel Analysis
• uOmnispace.CT Heart
• uOmnispace.CT Brain Perfusion
• uOmnispace.CT Dynamic Analysis
• uOmnispace.CT Liver Evaluation
• uOmnispace.CT Cardiovascular Combined Analysis
• uOmnispace.CT Body Perfusion

The modifications performed on the uOmnispace.CT (K233209) in this submission is due to the following changes that include:

• Add new application of Body Perfusion.
• Extend intended patient population for some applications
• Introduce deep-learning algorithm in applications of Lung Density Analysis, Vessel Analysis, Heart, Liver Evaluation and Cardiovascular Combined Analysis.

These modifications do not affect the intended use or alter the fundamental scientific technology of the device

This document describes the acceptance criteria and performance of the Medical Image Post-processing Software (uOmnispace.CT) for several AI-based segmentation algorithms, based on the provided FDA 510(k) clearance letter.

Acceptance Criteria and Reported Device Performance

Study Details for AI-Based Algorithms

The software features described in the submission are based on deep learning algorithms. The performance evaluation includes the following details for each application:

1. Lung Density Analysis (Lung Segmentation & Airway Segmentation)

• Sample size used for the test set and data provenance:

  • Sample Size: 100 subjects.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It notes the test dataset comprises 100 cases of Chest CT scans covering different gender, age, and anatomical variants.

• Number of experts used to establish the ground truth for the test set and their qualifications:

  • Number of Experts: Not explicitly stated as a specific number of individual experts. The process mentions "well-trained annotators" and "a senior clinical specialist" for review and modification.
  • Qualifications: "well-trained annotators" and "a senior clinical specialist" (no further details on experience provided).

• Adjudication method for the test set:

  • Ground truth annotations are initially done by "well-trained annotators." A "senior clinical specialist" then checks and modifies these annotations to ensure correctness. This implies a form of expert review and potential consensus or single expert finalization.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No, an MRMC comparative effectiveness study was not done. The evaluation focuses on standalone algorithm performance against ground truth.

• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes, the performance testing explicitly evaluates the algorithm's output (Dice coefficient) against a reference standard (ground truth), indicating a standalone performance evaluation.

• The type of ground truth used: Expert consensus, through a process of initial annotation by trained individuals and subsequent review/modification by a senior clinical specialist.

• The sample size for the training set: Not specified in the provided document. It only states that the training data is "independent of the data used to test the algorithm."

• How the ground truth for the training set was established: Not specified in the provided document. It only mentions the training data is independent from the test data.

2. Vessel Analysis (Automatic Bone Removal - Abdomen & Limbs, Head & Neck)

• Sample size used for the test set and data provenance:

  • Sample Size: 156 subjects.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It notes the test dataset comprises 156 cases of CTA scans covering different gender, age, and anatomical variants.

• Number of experts used to establish the ground truth for the test set and their qualifications:

  • Number of Experts: Not explicitly stated. The process mentions "well-trained annotators" and "a senior clinical specialist" for review and modification.
  • Qualifications: "well-trained annotators" and "a senior clinical specialist."

• Adjudication method for the test set: Similar to Lung Density Analysis, ground truth annotations are done by "well-trained annotators," with a "senior clinical specialist" checking and modifying them.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.

• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes.

• The type of ground truth used: Expert consensus.

• The sample size for the training set: Not specified.

• How the ground truth for the training set was established: Not specified.

3. Heart (Coronary Artery Extraction & Heart Chamber Segmentation)

• Sample size used for the test set and data provenance:

  • Sample Size: 72 subjects.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It notes the test dataset comprises 72 cases of CCTA scans covering different gender, age, and anatomical variants.

• Number of experts used to establish the ground truth for the test set and their qualifications:

  • Number of Experts: Not explicitly stated. The process mentions "well-trained annotators" and "a senior clinical specialist" for review and modification.
  • Qualifications: "well-trained annotators" and "a senior clinical specialist."

• Adjudication method for the test set: Similar to previous sections, ground truth annotations are done by "well-trained annotators," with a "senior clinical specialist" checking and modifying them.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.

• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes.

• The type of ground truth used: Expert consensus.

• The sample size for the training set: Not specified.

• How the ground truth for the training set was established: Not specified.

4. Liver Evaluation (Liver, Hepatic Artery, Hepatic Portal Vein, and Hepatic Vein Segmentation)

• Sample size used for the test set and data provenance:

  • Sample Size: 74 subjects for liver and hepatic artery segmentation; 80 subjects for hepatic portal vein and hepatic vein segmentation.
  • Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. It notes the test datasets comprise Chest CT scans covering different gender, age, and anatomical variants.

• Number of experts used to establish the ground truth for the test set and their qualifications:

  • Number of Experts: Not explicitly stated. The process mentions "well-trained annotators" and "a senior clinical specialist" for review and modification.
  • Qualifications: "well-trained annotators" and "a senior clinical specialist."

• Adjudication method for the test set: Similar to previous sections, ground truth annotations are done by "well-trained annotators," with a "senior clinical specialist" checking and modifying them.

• If a multi-reader multi-case (MRMC) comparative effectiveness study was done: No.

• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Yes.

• The type of ground truth used: Expert consensus.

• The sample size for the training set: Not specified.

• How the ground truth for the training set was established: Not specified.

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uWS-Angio Basic is intended to display and process XA (X-Ray Angiographic), CT (Computed Tomography), and MR (Magnetic Resonance) images that comply with the DICOM3.0 protocol.

uWS-Angio Basic is an image processing software that matches the use of medical vascular angiography X-ray machines. It contains the following functions:

• Patient Administration
• Review 2D: This application can be used to load 2D images and perform related processing. The function provides Basic processing tools for
  1. 2D image viewing,
  2. 2D image processing,
  3. 2D DSA image viewing and processing,
  4. Calibration and Measurement.
• Review 3D: This application can be used for loading and processing 3D data. The function provides Basic processing tools for
  1. 3D image viewing;
  2. 3D image processing;
  3. CTA bone removal.
• Saving
• Filming

uWS-Angio Basic can be deployed on independent hardware such as a stand-alone diagnostic review and post processing workstation. It can also be configured within a network to send and receive DICOM data. Furthermore, it can be deployed on systems of the United-imaging Angiography system family.

The provided FDA 510(k) clearance letter for the uWS-Angio Basic device does not contain the detailed performance data, acceptance criteria, or study design information typically found in a clinical study report or a more comprehensive technical and clinical assessment. The letter is a regulatory document affirming substantial equivalence to predicate devices, and while it mentions "Performance Testing" for certain algorithms, it only provides very high-level summaries and no specific quantitative acceptance criteria or detailed study results.

Therefore, I cannot extract the full information requested for acceptance criteria and the study proving the device meets them from this document. However, I can infer and state what is present and what is absent.

Here's an analysis based on the provided text, highlighting the limitations:

Information NOT available in the provided document:

• Detailed acceptance criteria with specific thresholds (e.g., AUPRC > 0.95, sensitivity > X%, specificity > Y%).
• Specific reported device performance metrics against those criteria.
• Sample size used for the test set.
• Country of origin of data, or whether it was retrospective/prospective.
• Number of experts used for ground truth, or their specific qualifications.
• Adjudication method for the test set.
• Whether a multi-reader multi-case (MRMC) comparative effectiveness study was done, or any effect size for human readers.
• Specific details on standalone algorithm performance (beyond a general statement that one performs "as well as" and another has "high accuracy").
• The type of ground truth used (e.g., pathology, outcomes data).
• The sample size for the training set.
• How the ground truth for the training set was established.

Information that can be inferred or is explicitly mentioned (though not in the requested format):

The document states:
"Testing was conducted between uWS-Angio Basic and the predicate device to evaluate the performance of the algorithms mentioned above. It is shown that catheter calibration of uWS-Angio Basic has high accuracy with average error rates consistently lower than those of the predicate device. CTA Removing Bone of the uWS-Angio Basic perform as well as the predicate devices."

This indicates that comparative performance against predicate devices was the primary method of demonstrating "safety and effectiveness," rather than meeting pre-defined quantitative performance metrics against a clinical ground truth.

Attempted Fulfilling of Request (with significant caveats due to missing information):

Given the limitations of the provided text, most of the requested table and details cannot be populated. The performance data section is extremely brief and qualitative.

1. Table of acceptance criteria and reported device performance:

2. Sample size used for the test set and data provenance:

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts:

• Number of experts: Not specified.
• Qualifications of experts: Not specified.
• Note: It's unclear if expert consensus was even the ground truth method, or if it was comparative to predicate output. This document does not suggest an independent ground truth for "accuracy" other than comparison to the predicate.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

• Not specified.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• No MRMC study is mentioned. The study described focuses on algorithm-to-algorithm comparison (uWS-Angio Basic vs. predicate device algorithms), not human-in-the-loop performance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:

• Yes, implicitly. The "Performance Testing" section describes evaluation of the "Catheter Calibration Algorithm for Review 2D" and "CTA Removing Bone Algorithm for Review 3D." This refers to the performance of these algorithms themselves, likely in a "standalone" fashion, by comparing their output to that of predicate device algorithms.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

• Not explicitly stated. The document implies a "ground truth" derived from the performance or output of the predicate devices. For example, "average error rates consistently lower than those of the predicate device" for catheter calibration suggests a definition of "accuracy" relative to the predicate's output. It's not stated that an independent, gold-standard clinical ground truth (e.g., pathology, clinical outcomes, or expertconsensus on the true measurement/absence/presence of bone) was established for the performance testing of these algorithms.

8. The sample size for the training set:

• Not specified. The document does not discuss the training of the algorithms.

9. How the ground truth for the training set was established:

• Not specified, as training set details are not provided.

Summary of what can be gleaned vs. what is missing:

This 510(k) clearance letter primarily focuses on demonstrating "substantial equivalence" based on similar intended use, technological characteristics, and a basic comparative performance assessment against already cleared predicate devices. It does not provide the detailed breakdown of quantitative acceptance criteria, robust clinical study design, or comprehensive ground truth establishment methods that would be seen for higher-risk devices or novel AI functions requiring extensive clinical validation. The "Performance Testing" described is rudimentary comparative testing without specified quantitative metrics or detailed study populations.

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