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Indications for Use:
The MAC 7 Resting ECG Analysis System is a non-invasive prescription device.

• The device is indicated for use to acquire, analyze, display and print electrocardiograms.
• The device is indicated for use to provide interpretation of the data for consideration by a physician.
• The device is indicated for use in a clinical setting, by a physician or by trained personnel who are acting on the orders of a licensed physician. It is not intended as a sole means of diagnosis.
• The interpretations of ECG offered by the device are only significant when used in conjunction with a physician over-read as well as consideration of all other relevant patient data.
• The device is indicated for use on adult and pediatric (birth through 21 years of age) populations.

Intended Use:
The MAC 7 Resting ECG Analysis System is intended to acquire, analyze, display, and record electrocardiographic information from adult or pediatric populations. Basic system simultaneously acquires data from each lead. Once the data is acquired, it can be analyzed, reviewed, stored, printed or transmitted. Transmission and reception of ECG data and other clinical data to and from a central clinical information system is optional.

The MAC 7 Resting ECG Analysis System is intended to be used under the direct supervision of a licensed healthcare practitioner, by trained operators in a hospital, medical professional's facility or wherever ECG testing is performed.

The MAC 7 Resting ECG Analysis System is a mobile electrocardiograph designed to acquire, analyze, display, and record ECG signals from surface ECG electrodes.

The device can capture 3, 6, 12 or 15 lead electrocardiograms, provide interpretive analysis, and print reports.

The device can connect to a network, either through a wired LAN connection or via wireless WiFi access points. Once on the network, the device can optionally interface with cardiology information systems such as the GEHC MUSE® system to participate in a complete electrocardiology workflow.

The device provides state-of-the-art information technology security features and a contemporary user interface. Mobility is provided via an optional trolley.

The FDA 510(k) clearance letter for the MAC 7 Resting ECG Analysis System (K251670) does not contain a specific study proving the device meets acceptance criteria. Instead, it establishes substantial equivalence to predicate devices (K203786, K173830, K210560) based on similarities in intended use, indications for use, technology, and performance, along with compliance with voluntary standards and non-clinical testing.

Therefore, the following information is extracted from the provided text to fulfill your request:

1. Acceptance Criteria and Reported Device Performance

The document describes the device's characteristics and compares them to predicate devices, demonstrating substantial equivalence rather than explicit acceptance criteria with numerical performance targets. The "Discussion of Differences" column often highlights that a change does not significantly affect substantial equivalence, implying that the performance remains acceptable.

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

The document states: "Summary of Clinical Tests: The subject of this premarket submission, MAC 7 Resting ECG Analysis System, did not require clinical studies to support substantial equivalence." This indicates that no specific test set data from clinical studies was used for performance evaluation in this submission. The "acceptance" is based on the device's technical characteristics aligning with or improving upon those of legally marketed predicate devices, supported by non-clinical testing and previous clearances for core components (like the 12SL™ analysis algorithm v24).

Therefore, details on sample size, country of origin, or retrospective/prospective nature of a clinical test set are not available in this document.

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

As no clinical studies were required for this submission to support substantial equivalence, there is no information provided regarding experts establishing ground truth for a test set.

4. Adjudication Method for the Test Set

Given that no clinical studies were performed, there is no adjudication method described for a test set.

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

The document does not mention any MRMC comparative effectiveness study. The focus is on establishing substantial equivalence to existing devices, not on demonstrating improved human reader performance with AI assistance.

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

The document states that the "Interpretive Statements are provided by 12SL (v24) ECG Analysis Program which was previously cleared under K221321." This implies that the performance of the 12SL™ algorithm itself (a standalone interpretation algorithm) would have been assessed during its prior clearance (K221321). However, the details of that standalone performance study are not included in this K251670 submission.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

Since no new clinical studies were performed for this submission, there is no mention of the type of ground truth used for a test set. For the 12SL™ analysis algorithm (v24) which provides interpretation statements, the ground truth would have been established during its prior clearance (K221321), but those details are not provided here.

8. The Sample Size for the Training Set

The document does not provide information on the sample size for any training set. As noted, the approval is based on substantial equivalence and non-clinical testing rather than specific training data for a new algorithm.

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

The document does not provide information on how ground truth was established for any training set.

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LOGIQ Totus is intended for use by a qualified physician for ultrasound evaluation of Fetal/Obstetrics; Abdominal(including Renal, Gynecology/Pelvic), Pediatric; Small organ(Breast, Testes, Thyroid); Neonatal Cephalic; Adult Cephalic; Cardiac(Adult and Pediatric), Peripheral Vascular, Musculo-skeletal Conventional and Superficial; Urology(including Prostate); Transrectal; Transvaginal; Transesophageal and Intraoperative(Abdominal and Vascular).

Modes of operation includes: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse, 3D/4D Imaging mode, Elastography, Shear Wave Elastography, Attenuation Imaging and Combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/PWD.

The LOGIQ Totus is intended to be used in a hospital or medical clinic.

The LOGIQ Totus is full featured, Track 3 device, primarily intended for general purpose diagnostic ultrasound system which consists of a mobile console approximately 490mm wide (monitor width: 545mm), 835mm deep and 1415~1815mm high that provides digital acquisition, processing and display capability. The user interface includes a computer keyboard, specialized controls, 14-inch LCD touch screen and color 23.8-inch LCD & HDU image display.

The provided FDA 510(k) clearance letter and summary for the LOGIQ Totus Ultrasound System (K253370) describes the acceptance criteria and the study for the Ultrasound Guided Fat Fraction for adult imaging (UGFF) software feature. This feature is being added to the LOGIQ Totus and is similar to a previously cleared Siemens UDFF feature.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document describes the performance of the UGFF feature by comparing it to MRI Proton Density Fat Fraction (MRI-PDFF) and, in a separate confirmatory study, to a predicate UDFF device. The "acceptance criteria" are implied by the reported strong correlations and limits of agreement with these reference standards.

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

• Primary Study (UGFF vs. MRI-PDFF):

  • Sample Size: 582 participants
  • Data Provenance: External clinical study in Japan (Population: Asian). The study was retrospective or prospective is not specified, but the phrase "obtained from the liver of five hundred and eighty-two (582) participants" suggests a data collection event rather than a purely retrospective analysis of existing medical records. The study is described as an "external clinical study," further suggesting a dedicated data collection.

• First Confirmatory Study (UGFF vs. MRI-PDFF):

  • Sample Size: 15 US patients and 5 EU patients (total 20 patients)
  • Data Provenance: US and EU patients. Demographic information on the 5 EU patients was unavailable. This was conducted as a "confirmatory study."

• Second Confirmatory Study (UGFF vs. UDFF):

  • Sample Size: 24 EU patients
  • Data Provenance: EU patients. This was conducted as a "confirmatory study."

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

The document does not specify the number of experts or their qualifications for establishing the ground truth.

4. Adjudication Method for the Test Set

The document does not specify an adjudication method. For the UGFF feature, the "ground truth" was objective measurements (MRI-PDFF or a predicate device's UDFF), which typically do not require adjudication by human experts in the same way an image diagnosis might.

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

No, an MRMC comparative effectiveness study was not done for the UGFF feature as described. The studies focused on comparing the device's output (UGFF index) to an objective reference standard (MRI-PDFF or another device's UDFF), not on how human readers' performance improved with or without AI assistance.

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

Yes, a standalone performance evaluation was done. The UGFF index, based on acoustic property measurements, is compared directly to MRI-PDFF and UDFF. This indicates the algorithm's performance independent of human interpretation or intervention in the final measurement calculation. While a technologist operates the ultrasound system, the UGFF index calculation itself is an algorithmic output.

7. The Type of Ground Truth Used

The type of ground truth used is MRI Proton Density Fat Fraction (MRI-PDFF) measurements, which are quantitative and objective reference standards for liver fat quantification. Additionally, for one confirmatory study, the ground truth was the Ultrasound-Derived Fat Fraction (UDFF) from a Siemens Acuson S3000/S2000, functioning as a predicate device's output. These are akin to "outcomes data" or "established reference standard measurements."

8. The Sample Size for the Training Set

The document states: "During the migration of the AI software feature from LOGIQ E10s (K231989), the algorithm was not retrained and there were no changes to the algorithmic flow or the AI components performing the inferencing." This implies the training set was associated with the original clearance of the Auto Renal Measure Assistant on the LOGIQ E10s (K231989) but the sample size for the training set is not provided in this document.

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

Similarly, since the algorithm was not retrained and the document pertains to the migration of an existing AI feature, the method for establishing the ground truth for the original training set is not provided in this document.

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LOGIQ Fortis is intended for use by a qualified physician for ultrasound evaluation of Fetal/Obstetrics; Abdominal (including Renal, Gynecology/Pelvic); Pediatric; Small Organ (Breast, Testes, Thyroid); Neonatal Cephalic; Adult Cephalic; Cardiac (Adult and Pediatric); Peripheral Vascular; Musculo-skeletal Conventional and Superficial; Urology (including Prostate); Transrectal; Transvaginal; Transesophageal and Intraoperative (Abdominal and Vascular).

Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse, 3D/4D Imaging mode, Elastography, Shear Wave Elastography, Attenuation Imaging and Combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/PWD.

The LOGIQ Fortis is intended to be used in a hospital or medical clinic.

The LOGIQ Fortis is a full featured, Track 3, general purpose diagnostic ultrasound system which consists of a mobile console approximately 575 mm wide (keyboard), 925 mm deep and 1300 mm high that provides digital acquisition, processing and display capability. The user interface includes a digital keyboard (physical keyboard as an option), specialized controls, 12-inch high-resolution color touch screen and 23.8-inch High Contrast LED LCD monitor (or 23.8-inch High Resolution LED LCD monitor as an option).

Here's a breakdown of the acceptance criteria and study details for the AI features of the LOGIQ Fortis Ultrasound System, based on the provided FDA 510(k) clearance letter:

AI Features Analyzed:

1. Auto Abdominal Color Assistant 2.0
2. Auto Aorta Measure Assistant
3. Auto Common Bile Duct (CBD) Measure Assistant
4. Ultrasound Guided Fat Fraction (UGFF)

1. Auto Abdominal Color Assistant 2.0

1.1. Acceptance Criteria and Reported Device Performance:

1.2. Sample Size and Data Provenance (Test Set):

• Number of individual subjects: 49
• Number of annotation images: 1186
• Country: USA (100%)
• Retrospective/Prospective: Not explicitly stated, but the description "Before the process of data annotation, all information displayed on the device is removed and performed on information extracted purely from Ultrasound B-mode images" and "Readers to ground truth the 'anatomy' visible in static B-Mode image" suggests retrospective analysis of collected images.

1.3. Number and Qualifications of Experts for Ground Truth (Test Set):

• Number of Experts: Not specified ("Readers to ground truth the 'anatomy'").
• Qualifications: Not specified (generally, these would be qualified ultrasonographers or radiologists).

1.4. Adjudication Method (Test Set): Not specified.

1.5. MRMC Comparative Effectiveness Study: No, this study evaluates the standalone performance of the AI model.

1.6. Standalone Performance: Yes, this study was done to evaluate the algorithm's performance in detecting abdominal structures.

1.7. Type of Ground Truth Used: Expert consensus on "anatomy" visible in static B-Mode images.

1.8. Sample Size for Training Set: Not explicitly stated, but implied to be separate from the test set.

1.9. How Ground Truth for Training Set Was Established: Not explicitly stated, but implied to be similar to the test set, with experts annotating B-mode images.

2. Auto Aorta Measure Assistant

2.1. Acceptance Criteria and Reported Device Performance:

2.2. Sample Size and Data Provenance (Test Set):

• Long View Aorta:
  • Subjects: 36 (11 Male, 25 Female)
  • Country: 16 Japan, 20 USA
• Short View Aorta:
  • Subjects: 35 (11 Male, 24 Female)
  • Country: 15 Japan, 20 USA
• Retrospective/Prospective: Not explicitly stated, but "Validation images were collected on LOGIQ Fortis" and the truthing process suggests retrospective analysis of collected images.

2.3. Number and Qualifications of Experts for Ground Truth (Test Set):

• Number of Experts: Not specified ("Readers to ground truth...").
• Qualifications: Not specified.

2.4. Adjudication Method (Test Set): An "arbitrator to select most accurate measurement among all readers" was used. This suggests a form of adjudication, possibly 2+1 or similar, where the arbitrator acts as the tie-breaker/final decision-maker.

2.5. MRMC Comparative Effectiveness Study: Yes, this study directly compares human performance with and without AI assistance by measuring keystrokes and accuracy.

• Effect Size (Keystrokes):
  • Long View Aorta: Reduction of ~2.896 keystrokes (4.132 - 1.236)
  • Short View Aorta: Reduction of ~4.743 keystrokes (7.05 - 2.307)
    (While not a traditional effect size like AUC improvement, this quantifies human workflow improvement).

2.6. Standalone Performance: Partially. The accuracy measurements compare AI baseline against an arbitrator's selected measurement, indicating standalone algorithm accuracy, but the primary focus is on human-in-the-loop efficiency.

2.7. Type of Ground Truth Used: Expert consensus on measurements, with an arbitrator for final selection.

2.8. Sample Size for Training Set: Not explicitly stated, but independence from the test set is ensured by "exam site origin."

2.9. How Ground Truth for Training Set Was Established: Not explicitly stated, but implied to be similar to the test set, with experts performing measurements.

3. Auto Common Bile Duct (CBD) Measure Assistant

3.1. Acceptance Criteria and Reported Device Performance:

3.2. Sample Size and Data Provenance (Test Set):

• Subjects: 25 (11 Male, 14 Female)
• Countries: USA (40%), Japan (60%)
• Retrospective/Prospective: Not explicitly stated, but "Validation images were collected on LOGIQ Fortis" and the truthing process suggests retrospective analysis of collected images.

3.3. Number and Qualifications of Experts for Ground Truth (Test Set):

• Number of Experts: Not specified ("Readers to ground truth...").
• Qualifications: Not specified.

3.4. Adjudication Method (Test Set): An "arbitrator to select most accurate measurement among all readers" was used.

3.5. MRMC Comparative Effectiveness Study: Yes, this study directly compares human performance with and without AI assistance by measuring keystrokes and accuracy (specifically, accuracy with and without segmentation scroll edit, which implies human interaction with AI).

• Effect Size (Keystrokes): Average reduction of 1.62 keystrokes.

3.6. Standalone Performance: Partially. The measurement accuracy with and without segmentation scroll edit provides insight into the algorithm's performance and the benefit of human refinement, but the primary focus is on human-in-the-loop efficiency and accuracy.

3.7. Type of Ground Truth Used: Expert consensus on measurements, with an arbitrator for final selection.

3.8. Sample Size for Training Set: Not explicitly stated, but independence from the test set is ensured by "exam site origin."

3.9. How Ground Truth for Training Set Was Established: Not explicitly stated, but implied to be similar to the test set, with experts performing measurements.

4. Ultrasound Guided Fat Fraction (UGFF)

4.1. Acceptance Criteria and Reported Device Performance:

4.2. Sample Size and Data Provenance (Test Set): This section describes the clinical studies used for validation of the UGFF index, rather than a traditional AI test set.

• Primary Study (Development/Validation of UGFF Index):

  • Subjects: 582 participants
  • Country: Japan
  • Retrospective/Prospective: "external clinical study in Japan" implies prospective data collection, with subsequent analysis.

• Second Confirmatory Study:

  • Subjects: 15 US patients, 5 EU patients (Total 20)
  • Country: US and EU
  • Retrospective/Prospective: Not specified.

• Third Confirmatory Study (Comparison with Predicate):

  • Subjects: 24 EU patients
  • Country: EU
  • Retrospective/Prospective: Not specified.

4.3. Number and Qualifications of Experts for Ground Truth (Test Set):

• UGFF Study: The ground truth is established by a reference imaging modality (MRI-PDFF) and a comparison device (UDFF), not directly by human experts interpreting ultrasound images for the purpose of the study. The study involves acoustic property measurements.

4.4. Adjudication Method (Test Set): Not applicable, as the ground truth is based on MRI-PDFF and UDFF, not human interpretation requiring adjudication.

4.5. MRMC Comparative Effectiveness Study: No, this is a clinical validation against a reference standard and a comparative study against a predicate device.

4.6. Standalone Performance: Yes, the UGFF index is an algorithm-generated value based on acoustic properties, and its performance is evaluated in a standalone manner against established reference methods (MRI-PDFF) and a legally marketed predicate (UDFF).

4.7. Type of Ground Truth Used: Quantitative measurements from a reference imaging modality (MRI Proton Density Fat Fraction - MRI-PDFF %) and a comparative device (Ultrasound-Derived Fat Fraction (UDFF, Siemens)).

4.8. Sample Size for Training Set: The UGFF index is based on a least squares fit (estimation) between acoustic property measurements and MRI-PDFF measurements from the primary 582-subject study. This study effectively serves as the "training" dataset for establishing the correlation and the estimation model.

4.9. How Ground Truth for Training Set Was Established: MRI-PDFF measurements were obtained from the liver of these 582 participants.

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AIR Recon DL is a deep learning based reconstruction technique that is available for use on GE HealthCare 1.5T, 3.0T, and 7.0T MR systems. AIR Recon DL reduces noise and ringing (truncation artifacts) in MR images, which can be used to reduce scan time and improve image quality. AIR Recon DL is intended for use with all anatomies, and for patients of all ages. Depending on the anatomy of interest being imaged, contrast agents may be used.

AIR Recon DL is a software feature intended for use with GE HealthCare MR systems. It is a deep learning-based reconstruction technique that removes noise and ringing (truncation) artifacts from MR images. AIR Recon DL is an optional feature that is integrated into the MR system software and activated through purchasable software option keys. AIR Recon DL has been previously cleared for use with 2D Cartesian, 3D Cartesian, and PROPELLER imaging sequences.

The proposed device is a modified version of AIR Recon DL that includes a new deep-learning phase correction algorithm for applications that create multiple intermediate images and combine them, such as Diffusion Weighted Imaging where multiple NEX images are collected and combined. This enhancement is an optional feature that is integrated into the MR system software and activated through an additional purchasable software option key (separate from the software option keys of the predicate device).

This document describes the acceptance criteria and the studies conducted to prove the performance of the AIR Recon DL device, as presented in the FDA 510(k) clearance letter.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Nonclinical Testing:
  • Phantom testing was conducted for the DLPC and PC-ARDL models. No specific sample size (number of phantom scans) is provided, but it implies a sufficient number for evaluation.
• In-Vivo Performance Testing:
  • ADC Accuracy: Diffusion-weighted brain images were acquired at 1.5T with b-values = 50, 400, 800, 1200 s/mm². The number of subjects is not explicitly stated, but it's referred to as "diffusion images" and "diffusion-weighted brain images."
  • Low-Contrast Detectability: Raw data from 4 diffusion-weighted brain scans were used.
• Quantitative Post Processing (Repeatability Study):
  • 6 volunteers were recruited. 2 volunteers scanned on a 1.5T scanner, 4 on a 3T scanner.
  • Scanned anatomical regions included brain, spine, abdomen, pelvis, and breast.
  • Each sequence was repeated 4 times.
  • Data Provenance: The document states "in-vivo data" and "volunteer scanning was performed simulating routine clinical workflows." This suggests prospective scanning of human subjects, likely in a controlled environment. The country of origin is not specified, but given the FDA submission, it's likely U.S. or international data meeting U.S. standards. The statement "previously acquired de-identified cases" for the Clinical Image Quality Study refers to retrospective data for that specific study, but the volunteer scanning for repeatability appears prospective.
• Clinical Image Quality Study:
  • 34 datasets of previously acquired de-identified cases.
  • Data Provenance: "previously acquired de-identified cases" indicates retrospective data. The country of origin is not specified.

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

• Nonclinical Testing: Ground truth established through phantom measurements and expected physical properties (e.g., signal bias, noise floor). No human experts involved in establishing ground truth here.
• In-Vivo Performance Testing:
  • ADC Accuracy: "Average ADC values were measured from regions of interest in the lateral ventricles." This implies expert selection of ROIs, but the number of experts is not specified. The ground truth for ADC is the expected isotropic Gaussian diffusion in these regions.
  • Low-Contrast Detectability: "The contrast ratio and contrast-to-noise ratio for each of the inserts were measured." This is a quantitative measure, not explicitly relying on expert consensus for ground truth on detectability, but rather on the known properties of the inserted synthetic objects.
• Quantitative Post Processing:
  • ADC Repeatability: Ground truth for repeatability is based on quantitative measurements and statistical analysis (coefficient of variability). ROI placement would typically be done by an expert, but the number is not specified.
  • Phase Correction Effectiveness: Ground truth is based on the theoretical expectation of signal distribution in real/imaginary channels after ideal phase correction.
• Clinical Image Quality Study:
  • One (1) U.S. Board Certified Radiologist was used.
  • Qualifications: "U.S. Board Certified Radiologist." No explicit number of years of experience is stated, but Board Certification indicates a high level of expertise.

4. Adjudication Method for the Test Set

• Nonclinical/Phantom Testing: No explicit adjudication method described beyond passing defined acceptance criteria for quantitative metrics.
• In-Vivo Performance Testing: Quantitative measurements (ADC values, contrast ratios, CNR) were used. Paired t-tests were conducted, which is a statistical comparison method, not an adjudication process as typically defined for expert readings.
• Quantitative Post Processing: Quantitative measurements and statistical analysis (coefficient of variability, comparison of real/imaginary channels).
• Clinical Image Quality Study: A single U.S. Board Certified Radiologist made the assessment. There is no stated adjudication method described, implying a single-reader assessment for clinical image quality.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• An MRMC comparative effectiveness study was not explicitly described as a formal study design in the provided text.
• The "Clinical Image Quality Study" involved only one radiologist, so it does not qualify as an MRMC study.
• There is no reported effect size of how much human readers improve with AI vs. without AI assistance. The study rather focused on the AI-reconstructed images' standalone diagnostic quality.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Yes, performance was evaluated in a standalone manner.
  • Nonclinical Testing: Phantom studies directly evaluate the algorithm's output against known physical properties and defined metrics.
  • In-Vivo Performance Testing: ADC accuracy and low-contrast detectability were measured directly from the reconstructed images, which is a standalone evaluation of the algorithm's quantitative output.
  • Quantitative Post Processing: Repeatability and effectiveness of phase correction in real/imaginary channels are algorithm-centric evaluations.
  • Even the clinical image quality study, while involving a human reader, assessed the standalone output of the algorithm (AIR Recon DL with Phase Correction) for diagnostic quality.

7. Type of Ground Truth Used

• Expert Consensus: Not explicitly stated as the primary ground truth for quantitative metrics, but one radiologist's assessment served as the primary clinical ground truth.
• Pathology: Not used as ground truth in the provided study descriptions. While some datasets "included pathological features such as prostate cancer... hepatocellular carcinoma," the assessment by the radiologist was on "diagnostic quality" of the images, not a comparison against pathology reports for definitive disease identification.
• Outcomes Data: Not used as ground truth.
• Other:
  • Physical Properties/Known Standards: For phantom testing (e.g., signal bias, noise floor, SNR, sharpness), and for theoretical expectations of ADC values in specific regions (lateral ventricles).
  • Known Synthetic Inserts: For low-contrast detectability.
  • Theoretical Expectations: For phase correction effectiveness (signal in real, noise in imaginary).

8. Sample Size for the Training Set

• The document does not provide any specific sample size for the training set used for the deep learning models (DLPC and PC-ARDL). It only states that the models are "deep learning-based."

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

• The document does not provide any information on how the ground truth for the training set was established. It only describes the testing of the final, trained models.

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The angiographic X-ray systems are indicated for use for patients from newborn to geriatric in generating fluoroscopic and rotational images of human anatomy for cardiovascular, vascular and non-vascular, diagnostic and interventional procedures.

Additionally, with the OR table, the angiographic X-ray systems are indicated for use in generating fluoroscopic and rotational images of human anatomy for image-guided surgical procedures. The OR table is suitable for interventional and surgical procedures.

GE HealthCare interventional x-ray systems are designed to perform monoplane fluoroscopic X-ray examinations to provide the imaging information needed to perform minimally invasive interventional X-Ray imaging procedures. Additionally, with an OR table, these systems allow to perform surgery and X-Ray image guided surgical procedures in a hybrid Operating Room.

Allia™ Moveo is a GE HealthCare interventional X-Ray system product model. It consists of a C-arm positioner, an X-ray table, an X-ray tube assembly, an X-ray power unit with its exposure control unit, an X-ray imaging chain (including a digital detector and an image processing unit).

Allia™ Moveo is a monoplane system (C-arm with mobile AGV gantry), with a square 41cm digital detector and the InnovaIQ table (with an option to make it an OR table).

Allia™ Moveo is an image acquisition system requiring connection to the GE HealthCare Advantage Workstation (AW) for 3D reconstruction. When a 3D acquisition is performed on the Allia™ Moveo system, the acquired 2D images are transferred to the Advantage Workstation (AW) to be processed by 3DXR (reference device K243446) for 3D reconstruction.

The purpose of this Premarket Notification is the introduction of a new C-arm with a modified detector mount.

N/A

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The device is a general purpose ultrasound system intended for use by qualified and trained healthcare professionals. Specific clinical applications remain the same as previously cleared: Fetal/OB; Abdominal (including GYN, pelvic and infertility monitoring/follicle development); Pediatric; Small Organ (breast, testes, thyroid etc.); Neonatal and Adult Cephalic; Cardiac (adult and pediatric); Musculo-skeletal Conventional and Superficial; Vascular; Transvaginal (including GYN); Transrectal

Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse, 3D/4D Imaging mode, Elastography, Shear Wave Elastography and Combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/ PWD, B/Elastography. The Voluson™ Expert 18, Voluson™ Expert 20, Voluson™ Expert 22 is intended to be used in a hospital or medical clinic.

The systems are full-featured Track 3 ultrasound systems, primarily for general radiology use and specialized for OB/GYN with particular features for real-time 3D/4D acquisition. They consist of a mobile console with keyboard control panel; color LCD/TFT touch panel, color video display and optional image storage and printing devices. They provide high performance ultrasound imaging and analysis and have comprehensive networking and DICOM capability. They utilize a variety of linear, curved linear, matrix phased array transducers including mechanical and electronic scanning transducers, which provide highly accurate real-time three-dimensional imaging supporting all standard acquisition modes.

The following probes are the same as the predicate: RIC5-9-D, IC5-9-D, RIC6-12-D, 9L-D, 11L-D, ML6-15-D, RAB6-D, C1-6-D, C2-9-D, M5Sc-D, RM7C, eM6CG3, RSP6-16-D , RIC10-D, 6S-D and L18-18iD, RIC12-D.

The existing cleared Probe C1-6-D is being added to previously cleared SW- AI Feature Sonolyst 1st Trimester.

The provided text describes the FDA 510(k) clearance for the Voluson Expert Series ultrasound systems, specifically focusing on the AI feature "Sonolyst 1st Trimester" and the addition of the C1-6-D transducer to this feature.

Here's an analysis of the acceptance criteria and the study that proves the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

Additional Performance Data (Mean values across transabdominal and transvaginal scans):

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

• SonoLyst 1st Trim IR: 7970 images

• SonoLyst 1st Trim X: 4931 images

• SonoLyst 1st Trim Live: 9111 images

• SonoBiometry CRL: 243 images

• Specific to Probegroup CL2 (which includes C1-6-D Probe): Data was collected from 396 patients.

• Data Provenance: Data was collected from multiple geographical sites including the UK, Austria, India, and USA. The data was collected using different systems (GE Voluson V730, P8, S6/S8, E6, E8, E10, Expert 22, Philips Epiq 7G).

• Retrospective/Prospective: The document does not explicitly state whether the test data was retrospective or prospective. However, the mention of "data acquired with transabdominal vs transvaginal probes" and "patients within the dataset includes pregnancies between 11 and 14 weeks of gestation, with no known fetal abnormalities at the time of imaging" suggests that the images were pre-existing or collected specifically for this evaluation, implying a retrospective or a pre-defined prospective collection for the study.

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

• Initial Curation: A single sonographer curated (sorted and graded) the images initially.
• Review Panel for Graded Images: Where the system's grading differed from the initial ground truth, the images were reviewed by a 5-sonographer review panel to determine the grading accuracy of the system.
• Qualifications: The document identifies them as "sonographers." Specific years of experience or expertise in fetal ultrasound are not provided, other than their role in image curation and review.

4. Adjudication Method for the Test Set

• Initial Sorting and Grading: Images were initially curated (sorted and graded) by a single sonographer.
• Reclassification during Sorting: The SonoLyst IR/X First Trimester process resulted in some images being reclassified during sorting based on the majority view of the panel (after the step where the system had sorted them).
• Grading Accuracy Review: For graded images where the initial single sonographer's ground truth differed from the system, a 5-sonographer review panel was used to determine the accuracy. This suggests an adjudication process where the panel formed a consensus or majority opinion to establish the final ground truth when discrepancies arose. The exact method (e.g., simple majority, weighted vote) is not specified beyond "majority view of the panel."

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

• The document does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done to evaluate how much human readers improve with AI vs. without AI assistance. The testing focused on the standalone performance of the AI algorithm against a ground truth established by sonographers.
• The verification of SonoLystLive 1st Trim Trimester features was based on the "average agreement between a sonographer panel and the output of the algorithm regarding Traffic light quality," which involves human readers assessing traffic light quality in relation to the algorithm's output, but it's not a study designed to measure human improvement with AI assistance.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

• Yes, a standalone performance evaluation was conducted. The performance metrics (SonoLystIR, SonoLystX, SonoLystLive, SonoBiometry CRL success rate) are reported as the accuracy of the algorithm comparing its output directly against the established ground truth. This is a measure of the algorithm's ability to perform its specified functions independently.

7. The Type of Ground Truth Used

• The ground truth was established through expert consensus/review by sonographers.
  • Initial curation by a single sonographer.
  • Review and reclassification during sorting based on the "majority view of the panel."
  • A 5-sonographer review panel was used to determine grading accuracy for discrepancies.
• The ground truth also adhered to standardized imaging protocols based on internationally recognized guidelines (AIUM Practice Parameter, AIUM Detailed Protocol, ISUOG Practice Guidelines, ISUOG Detailed Protocol, and the study by Yimei Liao et al.) which informed the quality and consistency of the expert review.

8. The Sample Size for the Training Set

• 122,711 labelled source images from 35,861 patients were used for training.

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

• The document states that "Data used for both training and validation has been collected across multiple geographical sites using different systems to represent the variations in target population."
• While the specific method for establishing ground truth for the training set is not explicitly detailed in the same way as the test set, it can be inferred that similar expert labeling and curation processes would have been applied given the emphasis on "labelled source images." The document focuses on the test set truthing process as part of verification, implying that the training data would have undergone a robust labeling process to ensure quality for machine learning.

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LOGIQ E10 is intended for use by a qualified physician for ultrasound evaluation of Fetal/Obstetrics; Abdominal (including Renal, Gynecology/Pelvic); Pediatric; Small Organ (Breast, Testes, Thyroid); Neonatal Cephalic, Adult Cephalic; Cardiac (Adult and Pediatric); Peripheral Vascular; Musculo-skeletal Conventional and Superficial; Urology (including Prostate); Transrectal; Transvaginal; Tranesophageal and Intraoperative (Abdominal and Vascular).

Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse, 3D/4D Imaging mode, Elastography, Shear Wave Elastography, Attenuation Imaging and combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/PWD.

The LOGIQ E10 is intended to be used in a hospital or medical clinic.

The LOGIQ E10 is a full featured, Track 3, general purpose diagnostic ultrasound system which consists of a mobile console approximately 585 mm wide (keyboard), 991 mm deep and 1300 mm high that provides digital acquisition, processing and display capability. The user interface includes a computer keyboard, specialized controls, 12-inch high-resolution color touch screen and 23.8-inch High Contrast LED LCD monitor.

Here's an analysis of the acceptance criteria and supporting studies for the LOGIQ E10 ultrasound system, derived from the provided FDA 510(k) Clearance Letter:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for Test Set and Data Provenance

• Auto Abdominal Color Assistant 2.0:
  • Test Set Sample Size: 49 individual subjects, 1186 annotation images.
  • Data Provenance: Retrospective, all data from the USA.
• Auto Aorta Measure Assistant:
  • Test Set Sample Size:
    • Long View Aorta: 36 subjects (11 Male, 25 Female).
    • Short View Aorta: 35 subjects (11 Male, 24 Female).
  • Data Provenance: Retrospective, from Japan (15-16 subjects) and USA (20 subjects).
• Auto Common Bile Duct (CBD) Measure Assistant:
  • Test Set Sample Size: 25 subjects (11 Male, 14 Female).
  • Data Provenance: Retrospective, from USA (40%) and Japan (60%).
• Ultrasound Guided Fat Fraction (UGFF):
  • Test Set Sample Size (Primary Study): 582 participants.
  • Data Provenance (Primary Study): Retrospective, Japan.
  • Test Set Sample Size (Confirmatory Study 1): 15 US patients + 5 EU patients (total 20).
  • Data Provenance (Confirmatory Study 1): Retrospective, USA and EU.
  • Test Set Sample Size (Confirmatory Study 2): 24 EU patients.
  • Data Provenance (Confirmatory Study 2): Retrospective, EU.

3. Number of Experts and Qualifications for Ground Truth

• Auto Abdominal Color Assistant 2.0: Not explicitly stated, but implies multiple "readers" to ground truth anatomical visibility. No specific qualifications are mentioned beyond "readers."
• Auto Aorta Measure Assistant: Not explicitly stated, but implies multiple "readers" for measurements and an "arbitrator" to select the most accurate measurement. No specific qualifications are mentioned beyond "readers" and "arbitrator."
• Auto Common Bile Duct (CBD) Measure Assistant: Not explicitly stated, but implies multiple "readers" for measurements and an "arbitrator" to select the most accurate measurement. No specific qualifications are mentioned beyond "readers" and "arbitrator."
• Ultrasound Guided Fat Fraction (UGFF): Ground truth for the primary study was MRI Proton Density Fat Fraction (MRI-PDFF %). No human experts were involved in establishing the ground truth for UGFF, as it relies on MRI-PDFF as the reference. The correlation between UGFF and UDFF also used UDFF as a reference, not human experts.

4. Adjudication Method for the Test Set

• Auto Abdominal Color Assistant 2.0: Not explicitly mentioned, however, the process described as "Readers to ground truth the 'anatomy' visible in static B-Mode image. (Before running AI)" and then comparing to AI predictions does not suggest an adjudication process for the ground truth generation itself beyond initial reader input. Confusion matrices were generated later.
• Auto Aorta Measure Assistant: An "Arbitrator" was used to "select most accurate measurement among all readers" for the initial ground truth, which was then compared to AI baseline. This implies a 1 (arbitrator) + N (readers) adjudication method for measurement accuracy. For keystroke comparison, readers measured with and without AI.
• Auto Common Bile Duct (CBD) Measure Assistant: An "Arbitrator" was used to "select most accurate measurement among all readers" for the initial ground truth, which was then compared to AI baseline. This implies a 1 (arbitrator) + N (readers) adjudication method for measurement accuracy. For keystroke comparison, readers measured with and without AI.
• Ultrasound Guided Fat Fraction (UGFF): Ground truth was established by MRI-PDFF or comparison to UDFF. No human adjudication method was described for these.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Auto Aorta Measure Assistant: Yes, a comparative study was performed by comparing keystroke counts with and without AI assistance for human readers.
  • Effect Size:
    • Long View Aorta AP Measurement: Average reduction from $4.132 \pm 0.291$ keystrokes (without AI) to $1.236 \pm 0.340$ keystrokes (with AI).
    • Short View Aorta AP and Trans Measurement: Average reduction from $7.05 \pm 0.158$ keystrokes (without AI) to $2.307 \pm 1.0678$ keystrokes (with AI).
• Auto Common Bile Duct (CBD) Measure Assistant: Yes, a comparative study was performed by comparing keystroke counts with and without AI assistance for human readers.
  • Effect Size: Average reduction of $1.62 \pm 0.375$ keystrokes (mean and standard deviation) from manual to AI-assisted measurements.
• Other features (Auto Abdominal Color Assistant 2.0, UGFF): The documentation does not describe a MRMC study for improved human reader performance with AI assistance for these features.

6. Standalone (Algorithm Only) Performance Study

• Auto Abdominal Color Assistant 2.0: Yes, the model's accuracy (detection accuracy, sensitivity, specificity, DICE score) was evaluated in a standalone manner against the human-annotated ground truth.
• Ultrasound Guided Fat Fraction (UGFF): Yes, the correlation and agreement of the UGFF algorithm's values were tested directly against an established reference standard (MRI-PDFF) and another device's derived fat fraction (UDFF).

7. Type of Ground Truth Used

• Auto Abdominal Color Assistant 2.0: Expert consensus/annotations on B-Mode images, followed by comparison to AI predictions.
• Auto Aorta Measure Assistant: Expert consensus on measurements (human readers with arbitrator selection) and keystroke counts from these manual measurements and AI-assisted measurements.
• Auto Common Bile Duct (CBD) Measure Assistant: Expert consensus on measurements (human readers with arbitrator selection) and keystroke counts from these manual measurements and AI-assisted measurements.
• Ultrasound Guided Fat Fraction (UGFF): Established clinical reference standard: MRI Proton Density Fat Fraction (MRI-PDFF %). For one confirmatory study, another cleared device's derived fat fraction (UDFF) was used as a comparative reference.

8. Sample Size for the Training Set

• The document states that "The exams used for test/training validation purpose are separated from the ones used during training process" but does not provide the sample size for the training set itself for any of the AI features.

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

• The document implies that the ground truth for training data would have been established similarly to the test data ground truth (e.g., expert annotation for Auto Abdominal Color Assistant, expert measurements for Auto Aorta/CBD Measure Assistants). However, the specific methodology for the training set's ground truth establishment (e.g., number of experts, adjudication, qualifications) is not detailed in the provided text. It only explicitly states that "Before the process of data annotation, all information displayed on the device is removed and performed on information extracted purely from Ultrasound B-mode images" for annotation. Independence of test and training data by exam site origin or overall separation is mentioned, but not the process for creating the training set ground truth.

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The LOGIQ E10s is intended for use by a qualified physician for ultrasound evaluation.

Specific clinical applications and exam types include: Fetal / Obstetrics; Abdominal (including Renal, Gynecology / Pelvic); Pediatric; Small Organ (Breast, Testes, Thyroid); Neonatal Cephalic; Adult Cephalic; Cardiac (Adult and Pediatric); Peripheral Vascular; Musculo-skeletal Conventional and Superficial; Urology (including Prostate); Transrectal; Transvaginal; Transesophageal and Intraoperative (Abdominal and Vascular).

Modes of operation include: B, M, PW Doppler, CW Doppler, Color Doppler, Color M Doppler, Power Doppler, Harmonic Imaging, Coded Pulse, 3D/4D Imaging mode, Elastography, Shear Wave Elastography, Attenuation Imaging and Combined modes: B/M, B/Color, B/PWD, B/Color/PWD, B/Power/PWD.

The LOGIQ E10s is intended to be used in a hospital or medical clinic.

The LOGIQ E10s is a full featured, Track 3, general purpose diagnostic ultrasound system which consists of a mobile console approximately 585 mm wide (keyboard), 991 mm deep and 1300 mm high that provides digital acquisition, processing and display capability. The user interface includes a computer keyboard, specialized controls, 12-inch high-resolution color touch screen and 23.8-inch High Contrast LED LCD monitor.

The provided text describes three AI features: Auto Abdominal Color Assistant 2.0, Auto Aorta Measure Assistant, and Auto Common Bile Duct (CBD) Measure Assistant, along with a UGFF Clinical Study.

Here's an analysis of the acceptance criteria and study details for each, where available:

1. Table of Acceptance Criteria and Reported Device Performance

For Auto Abdominal Color Assistant 2.0

For Auto Aorta Measure Assistant

For Auto Common Bile Duct (CBD) Measure Assistant

For UGFF Clinical Study

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

Auto Abdominal Color Assistant 2.0:

• Sample Size: 49 individual subjects (1186 annotation images)
• Data Provenance: Retrospective, from the USA (100%).

Auto Aorta Measure Assistant:

• Sample Size:
  • Long View Aorta: 36 subjects
  • Short View Aorta: 35 subjects
• Data Provenance: Retrospective, from Japan and USA.

Auto Common Bile Duct (CBD) Measure Assistant:

• Sample Size: 25 subjects
• Data Provenance: Retrospective, from USA (40%) and Japan (60%).

UGFF Clinical Study:

• Sample Size:
  • Original study: 582 participants
  • Confirmatory study (US/EU): 15 US patients and 5 EU patients (total 20)
  • Confirmatory study (UGFF vs UDFF): 24 EU patients
• Data Provenance: Retrospective and Prospective implicitly (clinical study implies data collection).
  • Original Study: Japan (Asian population)
  • Confirmatory Study (US/EU): US and EU (demographic info unavailable for EU patients, US patients: BMI 21.0-37.5, SCD 13.9-26.9)
  • Confirmatory Study (UGFF vs UDFF): EU

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

Auto Abdominal Color Assistant 2.0:

• Number of Experts: Not specified. The text mentions "Readers to ground truth the 'anatomy'".
• Qualifications of Experts: Not specified.

Auto Aorta Measure Assistant:

• Number of Experts: Not specified. The text mentions "Readers to ground truth the AP measurement..." and an "Arbitrator to select most accurate measurement among all readers." This implies multiple readers and a single arbitrator.
• Qualifications of Experts: Not specified.

Auto Common Bile Duct (CBD) Measure Assistant:

• Number of Experts: Not specified. The text mentions "Readers to ground truth the diameter..." and an "Arbitrator to select most accurate measurement among all readers." This implies multiple readers and a single arbitrator.
• Qualifications of Experts: Not specified.

UGFF Clinical Study:

• Number of Experts: Not applicable, as ground truth was established by MRI-PDFF measurements, not expert consensus on images.

4. Adjudication method for the test set

Auto Abdominal Color Assistant 2.0:

• Adjudication Method: Not explicitly described as a specific method (e.g., 2+1). The process mentions "Readers to ground truth" and then comparison to AI predictions, but no specific adjudication among multiple readers' initial ground truths.

Auto Aorta Measure Assistant:

• Adjudication Method: Implies an arbitrator-based method. "Arbitrator to select most accurate measurement among all readers." This suggests multiple readers provide measurements, and a single arbitrator makes the final ground truth selection.

Auto Common Bile Duct (CBD) Measure Assistant:

• Adjudication Method: Implies an arbitrator-based method. "Arbitrator to select most accurate measurement among all readers." Similar to the Aorta assistant.

UGFF Clinical Study:

• Adjudication Method: Not applicable. Ground truth was established by MRI-PDFF measurements.

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

Auto Abdominal Color Assistant 2.0:

• MRMC Study: Not explicitly stated as a comparative effectiveness study showing human improvement. The study focuses on the algorithm's performance against ground truth.
• Effect Size (Human Improvement with AI): Not reported.

Auto Aorta Measure Assistant:

• MRMC Study: Yes, an implicit MRMC study comparing human performance with and without AI. Readers performed measurements with and without AI assistance.
• Effect Size (Human Improvement with AI):
  • Long View Aorta (Keystrokes): Average keystrokes reduced from 4.132 (without AI) to 1.236 (with AI).
  • Short View Aorta (Keystrokes): Average keystrokes reduced from 7.05 (without AI) to 2.307 (with AI).
  • (No specific improvement in diagnostic accuracy for human readers with AI is stated, primarily focuses on efficiency via keystrokes).

Auto Common Bile Duct (CBD) Measure Assistant:

• MRMC Study: Yes, an implicit MRMC study comparing human performance with and without AI. Readers performed measurements with and without AI assistance.
• Effect Size (Human Improvement with AI):
  • Porta Hepatis CBD (Keystrokes): Average reduction in keystrokes for measurements with AI vs. manually is 1.62 +/- 0.375.
  • (No specific improvement in diagnostic accuracy for human readers with AI is stated, primarily focuses on efficiency via keystrokes).

UGFF Clinical Study:

• MRMC Study: No, this was a standalone algorithm performance study compared to a reference standard (MRI-PDFF) and a predicate device (UDFF). It did not involve human readers using the AI tool.

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

Auto Abdominal Color Assistant 2.0:

• Standalone Performance: Yes. The reported accuracy, sensitivity, specificity, and DICE score are for the algorithm's performance.

Auto Aorta Measure Assistant:

• Standalone Performance: Yes, implicitly. The "AI baseline measurement" was compared for accuracy against the arbitrator-selected ground truth. While keystrokes involved human interaction to use the AI, the measurement accuracy is an algorithm output.

Auto Common Bile Duct (CBD) Measure Assistant:

• Standalone Performance: Yes, implicitly. The "AI baseline measurement" was compared for accuracy against the arbitrator-selected ground truth.

UGFF Clinical Study:

• Standalone Performance: Yes. The study directly assesses the correlation and agreement of the UGFF algorithm's output with MRI-PDFF and another ultrasound-derived fat fraction algorithm.

7. The type of ground truth used

Auto Abdominal Color Assistant 2.0:

• Ground Truth Type: Expert consensus for anatomical visibility ("Readers to ground truth the 'anatomy' visible in static B-Mode image.")

Auto Aorta Measure Assistant:

• Ground Truth Type: Expert consensus from multiple readers, adjudicated by an arbitrator, for specific measurements ("Arbitrator to select most accurate measurement among all readers.")

Auto Common Bile Duct (CBD) Measure Assistant:

• Ground Truth Type: Expert consensus from multiple readers, adjudicated by an arbitrator, for specific measurements ("Arbitrator to select most accurate measurement among all readers.")

UGFF Clinical Study:

• Ground Truth Type: Outcomes data / Quantitative Reference Standard: MRI Proton Density Fat Fraction (MRI-PDFF %).

8. The sample size for the training set

Auto Abdominal Color Assistant 2.0:

• Training Set Sample Size: Not specified beyond "The exams used for test/training validation purpose are separated from the ones used during training process".

Auto Aorta Measure Assistant:

• Training Set Sample Size: Not specified beyond "The exams used for regulatory validation purpose are separated from the ones used during model development process".

Auto Common Bile Duct (CBD) Measure Assistant:

• Training Set Sample Size: Not specified beyond "The exams used for regulatory validation purpose are separated from the ones used during model development process".

UGFF Clinical Study:

• Training Set Sample Size: Not specified. The study describes validation but not the training phase.

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

Auto Abdominal Color Assistant 2.0:

• Training Set Ground Truth: Not explicitly detailed, but implied to be similar to the test set ground truthing process: "Information extracted purely from Ultrasound B-mode images" and "Readers to ground truth the 'anatomy'".

Auto Aorta Measure Assistant:

• Training Set Ground Truth: Not explicitly detailed, but implied to be similar to the test set ground truthing process: "Information extracted purely from Ultrasound B-mode images" and "Readers to ground truth the AP measurement...".

Auto Common Bile Duct (CBD) Measure Assistant:

• Training Set Ground Truth: Not explicitly detailed, but implied to be similar to the test set ground truthing process: "Information extracted purely from Ultrasound B-mode images" and "Readers to ground truth the diameter...".

UGFF Clinical Study:

• Training Set Ground Truth: Not specified for the training set, but for the validation set, the ground truth was MRI-PDFF measurements.

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