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Vantage Fortian/Orian 1.5T systems are indicated for use as a diagnostic imaging modality that produces cross-sectional transaxial, coronal, sagittal, and oblique images that display anatomic structures of the head or body. Additionally, this system is capable of non-contrast enhanced imaging, such as MRA.

MRI (magnetic resonance imaging) images correspond to the spatial distribution of protons (hydrogen nuclei) that exhibit nuclear magnetic resonance (NMR). The NMR properties of body tissues and fluids are:

• Proton density (PD) (also called hydrogen density)
• Spin-lattice relaxation time (T1)
• Spin-spin relaxation time (T2)
• Flow dynamics
• Chemical Shift

Depending on the region of interest, contrast agents may be used. When interpreted by a trained physician, these images yield information that can be useful in diagnosis.

The Vantage Fortian (Model MRT-1550/WK, WM, WO, WQ)/Vantage Orian (Model MRT-1550/U3, U4, U7, U8) is a 1.5 Tesla Magnetic Resonance Imaging (MRI) System. These Vantage Fortian/Orian models use 1.4 m short and 4.1 tons light weight magnet. They include the Canon Pianissimo™ Sigma and Pianissimo Zen technology (scan noise reduction technology). The design of the gradient coil and the whole-body coil of these Vantage Fortian/Orian models provide the maximum field of view of 55 x 55 x 50 cm and include the standard (STD) gradient system.

The Vantage Orian (Model MRT-1550/ UC, UD, UG, UH, UK, UL, UO, UP) is a 1.5 Tesla Magnetic Resonance Imaging (MRI) System. The Vantage Orian models use 1.4 m short and 4.1 tons light weight magnet. They include the Canon Pianissimo™ and Pianissimo Zen technology (scan noise reduction technology). The design of the gradient coil and the whole-body coil of these Vantage Orian models provide the maximum field of view of 55 x 55 x 50 cm. The Model MRT-1550/ UC, UD, UG, UH, UK, UL, UO, UP includes the XGO gradient system.

This system is based upon the technology and materials of previously marketed Canon Medical Systems MRI systems and is intended to acquire and display cross-sectional transaxial, coronal, sagittal, and oblique images of anatomic structures of the head or body. The Vantage Fortian/Orian MRI System is comparable to the current 1.5T Vantage Fortian/Orian MRI System (K240238), cleared April 12, 2024, with the following modifications.

Acceptance Criteria and Study for Canon Medical Systems Vantage Fortian/Orian 1.5T with AiCE Reconstruction Processing Unit for MR

This document outlines the acceptance criteria and the study conducted to demonstrate that the Canon Medical Systems Vantage Fortian/Orian 1.5T with AiCE Reconstruction Processing Unit for MR (V10.0) device meets these criteria, specifically focusing on the new features: 4D Flow, Zoom DWI, and PIQE.

The provided text focuses on the updates in V10.0 of the device, which primarily include software enhancements: 4D Flow, Zoom DWI, and an extended Precise IQ Engine (PIQE). The acceptance criteria and testing are described for these specific additions.

1. Table of Acceptance Criteria and Reported Device Performance

The general acceptance criterion for all new features appears to be demonstrating clinical acceptability and performance that is either equivalent to or better than conventional methods, maintaining image quality, and confirming intended functionality. Specific quantitative acceptance criteria are not explicitly detailed in the provided document beyond qualitative assessments and comparative statements.

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

• 4D Flow & Zoom DWI: Evaluated utilizing phantom images and "representative volunteer images." Specific numbers for volunteers are not provided.
• PIQE Clinical Image Review Study:
  • Subjects: A total of 75 unique subjects.
  • Scans: Comprising a total of 399 scans.
  • Reconstructions: Each scan was reconstructed multiple ways with or without PIQE, totaling 1197 reconstructions for scoring.
  • Data Provenance: Subjects were from two sites in USA and Japan. The study states that although the dataset includes subjects from outside the USA, the population is expected to be representative of the intended US population due to PIQE being an image post-processing algorithm that is not disease-specific and not dependent on factors like population variation or body habitus.

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

• PIQE Clinical Image Review Study:
  • Number of Experts: 14 USA board-certified radiologists/cardiologists.
  • Distribution: 3 experts per anatomy (Body, Breast, Cardiac, Musculoskeletal (MSK), and Neuro).
  • Qualifications: "USA board-certified radiologists/cardiologists." Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set

• PIQE Clinical Image Review Study: The study describes a randomized, blinded clinical image review study. Images reconstructed with either the conventional method or the new PIQE method were randomized and blinded to the reviewers. Reviewers scored the images independently using a modified 5-point Likert scale. Analytical methods used included Gwet's Agreement Coefficient for reviewer agreement and Generalized Estimating Equations (GEE) for differences between reconstruction techniques, implying a statistical assessment of agreement and comparison across reviewers rather than a simple consensus adjudication method (e.g., 2+1, 3+1).

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

• Yes, an MRMC comparative effectiveness study was done for PIQE.
• Effect Size of Human Readers' Improvement with AI vs. Without AI Assistance: The document states that "the Reviewers' scoring confirmed that: (a) PIQE generates higher spatial in-plane resolution images from lower resolution images (with the ability to triple the matrix dimensions in both in-plane directions, i.e. a factor of 9x); (b) PIQE contributes to ringing artifact reduction, denoising and increased sharpness; (c) PIQE is able to accelerate scanning by reducing the acquisition matrix only, while maintaining clinical matrix size and image quality; and (d) PIQE benefits can be obtained on regular clinical protocols without requiring acquisition parameter adjustment."
  • While it reports positive outcomes ("scored on average at, or above, clinically acceptable," "strong agreement at the 'good' and 'very good' level"), it does not provide a quantitative effect size (e.g., AUC difference, diagnostic accuracy improvement percentage) of how much human readers improve with AI (PIQE) assistance compared to without it. The focus is on the quality of PIQE-reconstructed images as perceived by experts, rather than the direct impact on diagnostic accuracy or reader performance metrics. It confirms that the performance is "similar or better" compared to conventional methods.

6. Standalone (Algorithm Only) Performance Study

• Yes, standalone performance was conducted for PIQE and other features.
  • 4D Flow and Zoom DWI: Evaluated using phantom images, which represents standalone, objective measurement of the algorithm's performance against known physical properties.
  • PIQE: Bench testing was performed on typical clinical images to evaluate metrics like Edge Slope Width (sharpness), Ringing Variable Mean (ringing artifacts), Signal-to-Noise ratio (SNR), and Contrast Ratio. This is an algorithmic-only evaluation against predefined metrics, without direct human interpretation as part of the performance metric.

7. Type of Ground Truth Used

• 4D Flow & Zoom DWI:
  • Phantom Studies: Known physical values (e.g., known flow values for velocity measurement, known distortion levels, known ADC values).
• PIQE:
  • Bench Testing: Quantitative imaging metrics derived from the images themselves (Edge Slope Width, Ringing Variable Mean, SNR, Contrast Ratio) are used to assess the impact of the algorithm. No external ground truth (like pathology) is explicitly mentioned here, as the focus is on image quality enhancement.
  • Clinical Image Review Study: Expert consensus/opinion (modified 5-point Likert scale scores from 14 board-certified radiologists/cardiologists) was used as the ground truth for image quality, sharpness, ringing, SNR, and feature conspicuity, compared against images reconstructed with conventional methods. No pathology or outcomes data is mentioned as ground truth.

8. Sample Size for the Training Set

The document explicitly states that the 75 unique subjects used in the PIQE clinical image review study were "separate from the training data sets." However, it does not specify the sample size for the training set used for the PIQE deep learning model.

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

The document does not provide information on how the ground truth for the training set for PIQE was established. It only mentions that the study test data sets were separate from the training data sets.

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This device is indicated to acquire and display cross-sectional volumes of the whole body (abdomen, pelvis, chest, extremities, and head) of adult patients.

TSX-501R has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

CT Scanner TSX-501R/1 V11.1 employs a next-generation X-ray detector unit (photon counting detector unit), which allows images to be obtained based on X-rays with different energy levels. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided 510(k) clearance letter.

It's important to note that a 510(k) summary typically doesn't provide the full, granular detail of a clinical study report. The information often indicates what was tested and the conclusion, but less about the specific methodologies, statistical thresholds for acceptance, or detailed performance metrics.

Understanding the Context: 510(k) Clearance

This document is a 510(k) clearance letter for a new CT scanner (CT Scanner TSX-501R/1 V11.1). The primary goal of a 510(k) submission is to demonstrate "substantial equivalence" to a legally marketed predicate device, not necessarily to prove absolute safety and effectiveness through extensive new clinical trials (which is more typical for a PMA - Premarket Approval). Therefore, the "acceptance criteria" and "study" described here are geared towards demonstrating this equivalence.

The core technology difference is the shift from an Energy Integrating Detector (EID) in the predicate to a Photon Counting Detector in the new device. The testing focuses on ensuring this new detector performs equivalently or better in terms of image quality and safety.

Acceptance Criteria and Reported Device Performance

Given the nature of a 510(k) for a CT scanner's hardware update (new detector), the "acceptance criteria" are implicitly tied to demonstrating equivalent or improved image quality and safety compared to the predicate device. The performance is assessed through bench testing with phantoms and review of clinical images.

Table of Acceptance Criteria and Reported Device Performance:

• Contrast-to-Noise Ratios (CNR)
• CT Number Accuracy
• Uniformity
• Pulse Pile Up
• Slice Sensitivity Profile (SSPz)
• Modulation Transfer Function (MTF)
• Standard Deviation of Noise and Pulse Pile
• Noise Power Spectra (NPS)
• Low Contrast Detectability (LCD) | "It was concluded that the subject device demonstrated equivalent or improved performance, compared to the predicate device, as demonstrated by the results of the above testing." |
  | Fundamental Properties of the Photon Counting Detector (using phantoms) | Effectiveness and equivalent performance compared to expected or predicate device for:
• Detector resolution and noise properties (MTF and DQE)
• Artifact analysis
• Count rate vs. current curve
• Pulse pileup or maximum count rate
• Lag/residual signal levels
• Stability over time
• Bad pixel map | "These bench studies utilized phantom data and achieved results demonstrative of equivalent performance in comparison with the predicate device." |
  | Clinical Image Quality (Human Review) | Reconstructed images using the subject device are of diagnostic quality. | "It was confirmed that the reconstructed images using the subject device were of diagnostic quality." |
  | Safety & Standards Conformance | Conformance to relevant electrical, radiation, software, and cybersecurity standards and regulations. | "This device is in conformance with the applicable parts of the following standards [list provided]... Additionally, this device complies with all applicable requirements of the radiation safety performance standards..." |
  | Risk Analysis & Verification/Validation | Established specifications for the device have been met, and risks are adequately managed. | "Risk analysis and verification/validation activities conducted through bench testing demonstrate that the established specifications for the device have been met." |
  | Software Documentation & Cybersecurity | Adherence to FDA guidance documents for software functions and cybersecurity. | "Software Documentation for a Basic Documentation Level... is included... Cybersecurity documentation... was included..." |

Study Details:

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

   • Test Set (Clinical Images): The specific number of clinical images/cases reviewed is not provided. The text states "Representative chest, abdomen, brain and MSK diagnostic images." This implies a selection of images from various body regions.
   • Data Provenance: The document does not specify the country of origin for the clinical images. It also does not explicitly state whether the data was retrospective or prospective, though for a 510(k) supporting equivalence, retrospective data collection for image review is common.

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

   • Number of Experts: The document states "reviewed by American Board-Certified Radiologists." The specific number is not provided.
   • Qualifications: "American Board-Certified Radiologists." This indicates a high level of qualification and experience in medical imaging interpretation.

3. Adjudication Method for the Test Set:

   • The document does not specify an adjudication method (like 2+1 or 3+1) for the clinical image review. It simply states they were "reviewed by American Board-Certified Radiologists" and "it was confirmed that the reconstructed images using the subject device were of diagnostic quality." This implies a consensus or individual assessment of diagnostic quality, but the process is not detailed.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • Was it done? No, a formal MRMC comparative effectiveness study demonstrating how human readers improve with AI vs. without AI assistance was not conducted or described for this submission. This makes sense as the device is a CT scanner itself, not an AI-assisted diagnostic software. The clinical image review was to confirm diagnostic quality of the images produced by the new scanner, not to assess reader performance with or without an AI helper.

5. Standalone (Algorithm Only) Performance:

   • Was it done? Yes, in a sense. The "bench testing" focusing on Objective Image Quality Evaluations and Fundamental Properties of the Photon Counting Detector can be considered "standalone" performance for the device's imaging capabilities. These tests used phantoms and measured technical specifications without human interpretation as the primary endpoint. The device's stated function is to acquire and display images, so its "standalone" performance is its ability to produce good images.

6. Type of Ground Truth Used:

   • Bench Testing (Phantoms): The ground truth is the physical properties of the phantoms and the expected performance characteristics based on established physics and engineering principles (e.g., a known object size for MTF, known density for CT number accuracy).
   • Clinical Images: The ground truth for confirming "diagnostic quality" is expert consensus/opinion from American Board-Certified Radiologists. It's an assessment of whether the image contains sufficient information and clarity for diagnostic purposes, not necessarily a comparison to a biopsy or long-term outcome.

7. Sample Size for the Training Set:

   • The document does not mention a training set in the context of typical AI/machine learning development. This device is a CT scanner hardware system, not an AI diagnostic algorithm that learns from training data. Therefore, the concept of a "training set" as it relates to AI models is not applicable here.

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

   • As stated above, the concept of a "training set" as applied to AI/machine learning development does not directly apply to this CT scanner hardware submission. The device's performance is based on its physical design and engineering, not on learning from a large dataset.

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The Diagnostic Ultrasound System Aplio i900 Model TUS-AI900, Aplio i800 Model TUS-AI800, and Aplio i700 Model TUS-AI700 are indicated for the visualization of structures, and dynamic processes with the human body using ultrasound and to provide image information for diagnosis in the following clinical applications: fetal, abdominal, intra-operative (abdominal), pediatric, small organs (thyroid, breast and testicle), trans-vaginal, trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatric), peripheral vascular, transesophageal, musculo-skeletal (both conventional and superficial), laparoscopic and Thoracic/Pleural. This system provides high-quality ultrasound images in the following modes: B mode, M mode, Continuous Wave, Color Doppler, Pulsed Wave Doppler, Power Doppler and Combination Doppler, as well as Speckle-tracking, Tissue Harmonic Imaging, Combined Modes, Shear wave, Elastography, and Acoustic attenuation mapping. This system is suitable for use in hospital and clinical settings by physicians or legally qualified persons who have received the appropriate training.

In addition to the aforementioned indications for use, when EUS transducer GF-UCT180 and BF-UC190F are connected, Aplio i800 Model TUS-AI800/E3 provides image information for diagnosis of the upper gastrointestinal tract and surrounding organs, airways, tracheobronchial tree and esophagus.

The Aplio i900 Model TUS-AI900, Aplio i800 Model TUS-AI800 and Aplio i700 Model TUS-AI700, V8.5 are mobile diagnostic ultrasound systems. These systems are Track 3 devices that employ a wide array of probes including flat linear array, convex, and sector array with frequency ranges between approximately 2MHz to 33MHz.

Based on the provided FDA 510(k) clearance letter for the Canon Medical Systems Aplio i900/i800/i700 Diagnostic Ultrasound System, Software V8.5, the document does not contain the detailed information required to describe specific acceptance criteria and the study that proves the device meets those criteria.

This document is a regulatory clearance letter, which affirms that the device is substantially equivalent to a previously cleared predicate device. It confirms that the manufacturer has submitted evidence to demonstrate this equivalence and that the FDA has found it acceptable. However, it does not explicitly provide the specific performance metrics (like sensitivity, specificity, accuracy, or specific measurements) that would typically constitute "acceptance criteria" for a novel AI/software feature, nor does it detail a study that would "prove" these criteria were met in the way one might expect for a new algorithmic claim.

The document states:

• "Risk Analysis and verification and validation activities demonstrate that the established specifications for these devices have been met."
• "Additional performance testing included in the submission was conducted in order to demonstrate that the requirements for the new transducers and improved software functionality were met."
• "The results of all these studies demonstrate that the subject devices meet established specifications and perform as intended and in accordance with labeling."

This indicates that internal testing was performed, and specifications were met, but the specifics of these tests, the acceptance criteria, and the raw performance data are not disclosed in this public clearance letter.

Therefore, I cannot fill in the requested table and answer many of the specific questions.

Here's an attempt to answer what can be inferred or directly stated from the provided text, with explicit notes about what is not present:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not provided in the document.
• Data Provenance (e.g., country of origin, retrospective or prospective): Not provided in the document.

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

• Number of Experts: Not provided in the document.
• Qualifications of Experts: Not provided in the document.

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

• Adjudication Method: Not provided in the document.

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

• MRMC Study: Not indicated or described in the document. The document focuses on modifications to existing software functionalities and new transducers, implying an update to an existing ultrasound system rather than a new AI-assisted diagnostic aid with a human-in-the-loop study.
• Effect Size: Not applicable as an MRMC study is not mentioned.

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

• Standalone Performance Study: Not explicitly described or detailed in the document. The submission mentions "improved software functionality" but does not present standalone performance metrics for any specific algorithm.

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

• Type of Ground Truth: Not provided in the document.

8. The sample size for the training set

• Sample Size for Training Set: Not provided in the document. The document discusses software improvements and migrations of existing functionalities, implying iterative development and validation rather than a new de novo AI model requiring a distinct training set description in this context.

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

• Ground Truth Establishment for Training Set: Not provided in the document.

Summary of what the document does provide regarding safety and effectiveness:

The clearance letter primarily establishes that the updated device (Aplio i900/i800/i700 Diagnostic Ultrasound System, Software V8.5) is substantially equivalent to its predicate device (Aplio i900/i800/i700, Diagnostic Ultrasound System, V8.1, K233195). This means the FDA has determined that the modifications (new transducers and software workflow/image quality improvements) do not raise new questions of safety or effectiveness and that the device performs as intended.

The safety assessment notes:

• Design and manufacturing under Quality System Regulations (21 CFR § 820) and ISO 13485 Standards.
• Conformance with applicable standards: ANSI AAMI ES60601-1, IEC 60601-1-2, IEC 60601-2-37, IEC 62304, IEC 62359, and ISO 10993-1.
• Risk Analysis, verification, and validation activities were performed.
• Cybersecurity documentation was included per FDA guidance.
• Software documentation appropriate for the Basic Documentation Level was included per FDA guidance.

In essence, while the document confirms that testing was done and specifications were met to achieve substantial equivalence, it does not disclose the granular details of those tests, the specific performance metrics (acceptance criteria), or the methodologies (like sample sizes, expert qualifications, or ground truth establishment) that would be present in a detailed performance study report.

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UltraExtend NX CUW-U001S Ultrasound Image Analysis Program is designed to allow the user to observe images and perform analysis based on examination data acquired using the following diagnostic ultrasound systems; TUS-AI900, TUS-AI800, and TUS-AI700.

This system is suitable for use in hospital and clinical settings by physicians or legally qualified persons who have received the appropriate training.

The UltraExtend NX, V2.0 is designed to allow the user to observe images and perform analysis based on examination data acquired using the Aplio i900/i800/i700 diagnostic ultrasound systems. RAW only or data saved in Image + RAW should be used for UltraExtend NX.

The FDA 510(k) clearance letter for the UltraExtend NX CUW-U001S V2.0 Ultrasound Image Analysis Program indicates that the device has integrated AI/ML-based functionality (2D Wall Motion Tracking with Full-assist function for left ventricle (LV) and Auto EF with Full-assist function for LV) that was previously cleared with a reference device (K223017). The submission states that "these studies utilized a representative subset of the clinical data acquired for the original performance testing of these features; additionally these studies applied the same acceptance criteria to evaluate the performance of these features compared to the same ground truth as utilized in the original performance evaluation of these features with the reference device."

Unfortunately, the provided text does not contain the specific acceptance criteria or detailed results of the performance testing for these AI/ML features. It only states that the features "perform as intended when integrated into the subject device, and with substantial equivalence as with the reference device."

Therefore, I cannot provide a table of acceptance criteria and reported device performance or many of the specific details requested in your prompt based solely on the provided document. The document refers to the original performance testing of the reference device (K223017) for these details.

However, I can extract and infer information about the study design to the extent possible:

Here's what can be inferred from the provided text, and what cannot be determined:

Acceptance Criteria and Device Performance

• The document states that the same acceptance criteria as the original performance testing for the reference device (K223017) were applied.
• Cannot Determine: The specific numerical acceptance criteria (e.g., specific accuracy, sensitivity, specificity thresholds) or the reported device performance metrics (e.g., actual accuracy, sensitivity, specificity values) are not provided in this document.

Study Information

Summary of missing information:

To fully answer your prompt, you would need to consult the original 510(k) submission for the reference device (K223017), Aplio i900/i800/i700 Diagnostic Ultrasound System, Software Version 7.0, as that is where the detailed performance data, acceptance criteria, and ground truth establishment methodology for the AI/ML features would have been submitted and evaluated by the FDA. The current document (K250328) focuses on demonstrating that these already cleared AI/ML features maintain their performance when integrated into a new workstation.

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Vantage Galan 3T systems are indicated for use as a diagnostic imaging modality that produces crosssectional transaxial, coronal, sagittal, and oblique images that display anatomic structures of the head or body. Additionally, this system is capable of non-contrast enhanced imaging, such as MRA.

MRI (magnetic resonance imaging) images correspond to the spatial distribution of protons (hydrogen nuclei) that exhibit nuclear magnetic resonance (NMR). The NMR properties of body tissues and fluids are:

·Proton density (PD) (also called hydrogen density)

·Spin-lattice relaxation time (T1)

·Spin-spin relaxation time (T2)

·Flow dynamics

·Chemical Shift

Depending on the region of interest, contrast agents may be used. When interpreted by a trained physician, these images yield information that can be useful in diagnosis.

The Vantage Galan (Model MRT-3020) is a 3 Tesla Magnetic Resonance Imaging (MRI) System, previously cleared under K241496. This system is based upon the technology and materials of previously marketed Canon Medical Systems and is intended to acquire and display crosssectional transaxial, coronal, sagittal, and oblique images of anatomic structures of the head or body.

This document describes a 510(k) premarket notification for the Vantage Galan 3T, MRT-3020, V10.0 with AiCE Reconstruction Processing Unit for MR. This submission concerns a modification to an already cleared device, primarily involving the addition of a standard gradient system and the extension of the Precise IQ Engine (PIQE) to new scan families, weightings, and anatomical areas.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of quantitative acceptance criteria for PIQE performance. Instead, it describes acceptance in qualitative terms based on expert review.

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

• Test Set Sample Size: 106 unique subjects.
• Data Provenance: Two sites in the USA and one in Japan. This data is described as "separate from the training data sets." The document states that the multinational study population is expected to be representative of the intended US population for PIQE, as PIQE is an image post-processing algorithm not disease-specific or dependent on acquisition parameters that might be affected by population variation. Comparisons were internal (each subject as its own control).

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

• Number of Experts: 14 USA board-certified radiologists and cardiologists (3 reviewers per anatomy).
• Qualifications: "USA board-certified radiologists and cardiologists." Specific experience levels (e.g., years of experience) are not provided.

4. Adjudication Method for the Test Set

The document describes a scoring process by multiple reviewers but does not specify a formal adjudication method (e.g., 2+1, 3+1). It states: "scored by 3 reviewers per anatomy in various clinically-relevant categories... Reviewer scoring data was analyzed for reviewer agreement and differences between reconstruction techniques using Gwet's Agreement Coefficient and Generalized Estimating Equations, respectively." This suggests that the scores from the three reviewers were aggregated and analyzed statistically, rather than undergoing a consensus or tie-breaking adjudication process for each individual case.

5. 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: Yes, a multi-site, randomized, blinded clinical image review study was conducted.
• Effect Size (AI-assisted vs. without AI assistance): This was not an AI-assisted reader study. The study compared images reconstructed with the conventional method (matrix expansion with Fine Reconstruction and typical clinical filter) against images reconstructed with PIQE. The purpose was to evaluate the image quality produced by PIQE, not to assess reader performance with or without AI assistance. Therefore, no effect size on human reader improvement with AI assistance is reported. The study aimed to demonstrate that PIQE-reconstructed images are clinically acceptable and offer benefits like sharpness and ringing reduction.

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

Yes, a standalone performance evaluation of the PIQE algorithm was conducted through "bench testing." This involved evaluating metrics like Edge Slope Width, Ringing Variable Mean, Signal-to-Noise ratio, and Contrast Change Ratio on typical clinical images from various anatomical regions. This bench testing demonstrated that PIQE "generates images with sharper edges while mitigating the smoothing and ringing effects and maintaining similar or better contrast and SNR."

7. The Type of Ground Truth Used

• For Bench Testing: The "ground truth" implicitly referred to established quantitative image quality metrics (Edge Slope Width, Ringing Variable Mean, Signal-to-Noise ratio, and Contrast Change Ratio) and comparisons against conventional reconstruction methods.
• For Clinical Image Review Study: The "ground truth" was established by expert consensus/evaluation, where 14 board-certified radiologists and cardiologists scored images on various clinically-relevant categories (ringing, sharpness, SNR, overall IQ, and feature conspicuity) using a modified 5-point Likert scale.

8. The Sample Size for the Training Set

The document explicitly states that the "106 unique subjects... from two sites in USA and one in Japan... were scanned... to provide the test data sets (separate from the training data sets)." The sample size for the training set is not provided in the document.

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

The document does not provide information on how the ground truth for the training set was established, as details about the training set itself are omitted.

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This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head, with the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, pancreas, etc. The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software/hardware, of the whole organ by a trained and qualified physician.

FIRST is an iterative reconstruction algorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac, extremities and head applications.

AiCE is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, cardiac, extremities, head, and inner ear applications.

The spectral imaging system allows the system to acquire two nearly simultaneous CT images of an anatomical location using distinct tube voltages and/or tube currents by rapid KV switching. The Xray dose will be the sum of the dose at each respective tube voltage and current in a rotation. Information regarding the material composition of various organs, tissues, and contrast materials may be gained from the differences in X-ray attenuation between these distinct energies. When used by a qualified physician, a potential application is to determine the course of treatment.

PIQE* is a Deep Learning Reconstruction method designed to enhance spatial resolution. By incorporating noise reduction into the Deep Convolutional Network (DCNN), it is possible to achieve both spatial resolution improvement and noise reduction for cardiac, abdomen and pelvis, and lung applications, in comparison to FBP and hybrid iterative reconstruction.

CLEAR Motion is a Deep Learning Reconstruction (DLR) method designed to reduce motion artifacts. A Deep Convolutional Network (DCNN) is used to estimate the patient's motion. This information is used in the reconstruction process to obtain lung images with less motion artifacts.

Aquilion ONE (TSX-308A/3) V1.5 is a whole body multi-slice helical CT scanner, consisting of a gantry, couch and a console used for data processing and display. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state quantitative acceptance criteria in a dedicated section. However, it implicitly defines performance through comparisons to a predicate device and statements about image quality.

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

The document mentions the use of "phantoms" for image quality evaluations and "clinical images" for performance testing.

• Phantom Studies:
  • Sample Size: Not explicitly stated, but multiple phantoms were used (e.g., water phantom, thoracic dynamic phantom). The exact number of scans or reconstructed images from these phantoms is not provided.
  • Data Provenance: Not explicitly stated, but phantom studies typically involve controlled, non-clinical data generation.
• Clinical Image Evaluations:
  • Sample Size: Not explicitly stated; "Representative body, cardiac, chest, head, and extremity diagnostic images" were used. The exact number of cases is not provided.
  • Data Provenance: Implied to be retrospective clinical data, as they are "obtained using the subject device" and "reviewed by American Board-Certified Radiologists." No country of origin is specified.

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

• Number of Experts: Not explicitly stated for each specific evaluation. For clinical image evaluation, it states "American Board-Certified Radiologists" (plural), indicating more than one.
• Qualifications of Experts: "American Board-Certified Radiologists." No specific years of experience are mentioned.

4. Adjudication Method for the Test Set:

• Clinical Images: For the clinical image quality evaluation, it states "reviewed by American Board-Certified Radiologists." It doesn't specify an adjudication method (e.g., 2+1, 3+1, none). It implies a consensus or individual assessment to confirm diagnostic quality.
• Phantom Studies: Phantoms have inherent, objective ground truth based on their design and known properties, so expert adjudication isn't typically applicable in the same way.

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

• No, an MRMC comparative effectiveness study was not explicitly described in the provided text. The document focuses on showing substantial equivalence through phantom studies and a general statement about diagnostic quality of clinical images, rather than a comparative study of human readers with and without AI assistance to quantify improvement.

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

• Yes, standalone performance was evaluated. The "Image Quality Evaluations" and "CLEAR Motion Evaluations" using phantoms are examples of standalone performance testing. These tests assess the device's algorithms (PIQE, CLEAR Motion) directly against objective metrics or by comparing reconstructed images for specific features (e.g., noise reduction, motion artifact reduction) without human intervention in the diagnostic process.

7. Type of Ground Truth Used:

• For Phantom Studies (PIQE, CLEAR Motion): Objective ground truth derived from the known physical properties and design of the phantoms (e.g., known image metrics, controlled motion patterns).
• For Clinical Image Quality: Expert consensus/review by "American Board-Certified Radiologists" to confirm "diagnostic quality."

8. Sample Size for the Training Set:

• Not provided. The document describes the device, its features (some of which use Deep Learning Reconstruction), and details of performance testing. It does not include information about the size or nature of the training data used for the AI algorithms (AiCE, PIQE, CLEAR Motion).

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

• Not provided. As the training set details are absent, the method for establishing its ground truth is also not mentioned.

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The Diagnostic Ultrasound System Aplio i900 Model TUS-A1900, Aplio i800 Model TUS-A1800 and Aplio i700 Model TUS-AI700 are indicated for the visualization of structures, and dynamic processes with the human body using ultrasound and to provide image information for diagnosis in the following clinical applications: fetal, abdominal, intra-operative (abdominal), pediatric, small organs (thyroid, breast and testicle), trans-rectal, neonatal cephalic, adult cephalic, cardiac (both adult and pediatic), peripheral vascular, transesophageal, musculo-skeletal (both conventional and superficial), laparoscopic and Thoracic/Pleural. This system provides high-quality ultrasound images in the following modes B mode, M mode, Continuous Wave, Color Doppler, Pulsed Wave Doppler and Combination Dopler, as well as Speckle-tracking, Tissue Harmonic Imaging, Combined Modes, Shear wave, Elastography, and Acoustic attenuation mapping. This system is suitable for use in hospital and clinical settings by physicians or legally qualified persons who have received the appropriate training.

The Aplio i900 Model TUS-AI900, Aplio i800 Model TUS-AI800 and Aplio i700 Model TUS-AI700, V7.0 are mobile diagnostic ultrasound systems. These systems are Track 3 devices that employ a wide array of probes including flat linear array, convex, and sector array with frequency ranges between approximately 2MHz to 33MHz.

The document describes the validation of several AI/ML-based features within the Aplio i900/i800/i700 Diagnostic Ultrasound System, Software V7.0. The study aims to demonstrate that these new features are substantially equivalent to existing functionalities and improve workflow.

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

The document describes several AI/ML-based features. While the format isn't a single table, I can synthesize the information for each feature:

Feature: Auto Plane Detection

Feature: Quick Strain

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This device is a digital radiography/fluoroscopy system used interventional angography configuration. The system is indicated for use in diagnostic and angiographic procedures for blood vessels in the heart, brain, abdomen, and lower extremities.

The Alphenix, INFX-8000V/B, INFX-8000V/S, V9.5, is an X-ray system that is capable of radiographic and fluoroscopic studies and is used in an interventional setting. The system consists of a C-arm/ Ω-arm which is equipped with an X-ray tube, beam limiter and X-ray receptor, X-ray controller, computers with system and processing software, and a patient radiographic table.

The provided text is a 510(k) summary for the Alphenix, INFX-8000V/B, INFX-8000V/S, V9.5. This document focuses on demonstrating substantial equivalence to a predicate device (Alphenix, V9.1) due to software modifications (V9.1 to V9.5) and integrating previously cleared functions/components.

Crucially, the document explicitly states there are no new indications for use or intended uses of the device, and that the basic system configuration, method of operation, base software, and manufacturing process remain unchanged.

Because the submission is for software changes to an existing device, and not for a new AI/ML-driven diagnostic or assistive device that would require performance validation against clinical endpoints and specific acceptance criteria related to diagnostic accuracy, the provided text does not contain the detailed information necessary to answer points 1-9 of your request.

The study described is not a performance study in the sense of evaluating the diagnostic accuracy of an AI algorithm. Instead, it is a verification/validation testing to demonstrate that the established specifications for the modified device (software update) have been met, and that the device remains as safe and effective as its predicate.

Here's a breakdown of why the requested information is not present and what is stated:

Information Not Present in the Provided Text:

• 1. A table of acceptance criteria and the reported device performance: This would be relevant for a device with a new diagnostic or assistive function whose performance needs to be validated (e.g., sensitivity, specificity, AUC for an AI algorithm detecting disease). For software updates to an existing interventional fluoroscopic X-ray system, the "performance" validated is typically functional and safety-related, not diagnostic accuracy.
• 2. Sample size used for the test set and the data provenance: Not applicable as it's not a diagnostic performance study.
• 3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable.
• 4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
• 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: Not applicable, as this is not an AI-assisted diagnostic device submission.
• 6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
• 7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable.
• 8. The sample size for the training set: Not applicable, as it's not an AI/ML device being developed and trained.
• 9. How the ground truth for the training set was established: Not applicable.

What is stated regarding testing and acceptance:

The document indicates that the acceptance criteria are related to safety and functionality of the X-ray system after the software update, ensuring it remains substantially equivalent to the predicate device.

• Testing Method: "Risk analysis and verification/validation testing conducted through bench testing demonstrate that the established specifications for the device have been met."
• Conformance to Standards: "Testing of the modified system was conducted in accordance with the applicable standards published by the International Electromechanical Commission (IEC) for Medical Devices and XR Systems."
  • Applicable standards listed include: IEC 60601-1, IEC 60601-1-2, IEC 60601-1-3, IEC 60601-1-6, IEC 60601-2-28, IEC 60601-2-43, IEC 62304, IEC 62366-1, IEC 81001-5-1, ISO 17664-2, IEC TR 60601-4-2.
• Grounds for Conclusion of Substantial Equivalence: "Based upon this information, conformance to standards, successful completion of software validation, application of risk management and design controls presented in this submission it is concluded that the subject device is substantially equivalent in safety and effectiveness to the predicate device."
• Software Validation: "A Basic Documentation Level was determined, per the FDA guidance document, 'Content of Premarket Submissions for Device Software Functions' issued on June 14, 2023, is also included as part of this submission." This indicates that software validation was performed according to FDA guidance for medical device software.

In summary, the provided document is a 510(k) for a software update to an existing fluoroscopic X-ray system, asserting substantial equivalence based on safety, functionality, and compliance with general medical device and X-ray specific standards, rather than a clinical performance study of a novel AI diagnostic algorithm.

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Vantage Galan 3T systems are indicated for use as a diagnostic imaging modality that produces cross-sectional transaxial, coronal, sagittal, and oblique images that display anatomic structures of the head or body. Additionally, this system is capable of non-contrast enhanced imaging, such as MRA.

MRI (magnetic resonance imaging) images correspond to the spatial distribution of protons (hydrogen nuclei) that exhibit nuclear magnetic resonance (NMR). The NMR properties of body tissues and fluids are:

·Proton density (PD) (also called hydrogen density)

·Spin-lattice relaxation time (T1)

·Spin-spin relaxation time (T2)

·Flow dynamics

·Chemical Shift

Depending on the region of interest, contrast agents may be used. When interpreted by a trained physician, these images yield information that can be useful in diagnosis.

The Vantage Galan (Model MRT-3020) is a 3 Tesla Magnetic Resonance Imaging (MRI) System, previously cleared under K230355. This system is based upon the technology and materials of previously marketed Canon Medical Systems and is intended to acquire and display crosssectional transaxial, coronal, sagittal, and oblique images of anatomic structures of the head or body.

The provided document describes a 510(k) premarket notification for a modified MRI system (Vantage Galan 3T, MRT-3020, V10.0 with AiCE Reconstruction Processing Unit for MR) by Canon Medical Systems Corporation. The primary purpose of this submission is to demonstrate substantial equivalence to a previously cleared predicate device (Vantage Galan 3T, MRT-3020, V9.0 with AiCE Reconstruction Processing Unit for MR, K230355) despite hardware and software changes.

The document primarily focuses on verifying that the changes do not adversely affect the device's safety and effectiveness and that the modified device maintains performance comparable to the predicate. It does not describe a study proving the device meets specific acceptance criteria in the context of diagnostic accuracy, particularly for an AI-assisted diagnostic device, as the "AiCE Reconstruction Processing Unit" is for image reconstruction, not for AI-based diagnosis.

Therefore, many of the requested fields related to diagnostic performance studies (like multi-reader multi-case studies, expert consensus ground truth, effect size of AI assistance for human readers, or standalone AI performance) are not applicable or not provided in this regulatory submission, as this is a modification of an imaging device itself, not a new AI diagnostic algorithm.

Based on the provided text, here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a formal table of "acceptance criteria" for diagnostic accuracy or clinical utility that an AI diagnostic algorithm would typically have, nor does it report performance metrics against such criteria. Instead, the testing focuses on ensuring the new features and hardware maintain image quality, safety, and functionality comparable to the predicate device.

However, the document does list testing performed for new features. We can infer the "acceptance criteria" for these were successful confirmation of functionality and image quality.

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

• 4D Flow: "a phantom with known flow values" and "volunteers." Specific numbers are not provided.
• Zoom DWI: "phantom images" and "representative volunteer images." Specific numbers are not provided.
• 3D-QALAS: "multiple volunteers." Specific numbers are not provided.
• Data Provenance: Not explicitly stated, but given Canon Medical Systems Corporation is based in Japan (manufacturer) and the U.S. (agent), it's likely a mix or either. The studies are described as "bench testing" and using "volunteers," implying prospective data collection for these specific tests.

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

• 3D-QALAS: "three experienced reviewers" compared images. Their specific qualifications (e.g., "radiologist with 10 years of experience") are not detailed, but their role as "reviewers" suggests they are professionals qualified to assess image quality.
• Other features (4D Flow, Zoom DWI): The ground truth appears to be established by comparison to known phantom values or visual confirmation of expected image quality improvements (e.g., artifact suppression for Zoom DWI). No external "experts" beyond the testing team are mentioned for establishing ground truth in these cases, which is typical for image quality and functional assessments.

4. Adjudication Method for the Test Set

• For 3D-QALAS, comparison was made by "three experienced reviewers." The document does not specify an adjudication method (e.g., 2+1, 3+1 consensus). It simply states they "compared" the images.
• For other features, adjudication methods are not applicable as the "ground truth" relies on phantom measurements or visual confirmation against expected technical performance.

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

No, a MRMC study comparing human readers with and without AI assistance was not reported. This submission concerns hardware and image reconstruction software changes for an MRI system, not an AI diagnostic algorithm intended for human reader assistance in interpretation. The "AiCE Reconstruction Processing Unit" processes raw MR data into images, it does not interpret those images for diagnostic findings. Therefore, the effect size of human readers improving with AI vs without AI assistance is not relevant or measured here.

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

This refers to the performance of the image reconstruction itself. The testing described (e.g., for 4D Flow, Zoom DWI, 3D-QALAS) demonstrates the standalone technical performance of these new imaging capabilities and the AiCE reconstruction unit in producing images with desired characteristics (e.g., flow visualization, artifact suppression, specific contrast weighting). The "performance" is that the images are generated accurately according to the algorithms' design and meet technical quality metrics.

7. The Type of Ground Truth Used

• 4D Flow: Phantom with "known flow values" (objective physical ground truth) and visual assessment from "volunteer images."
• Zoom DWI: Phantom images and visual assessment from "volunteer images" (technical image quality and accuracy of ADC values).
• 3D-QALAS: Comparison against "reference images published in the literature" (literature-based reference) and assessment by "three experienced reviewers" on image quality metrics (expert qualitative assessment against a standard).
• General System Performance: Compliance with recognized consensus standards (e.g., IEC, NEMA) and comparison to the characteristics of the predicate device (regulatory/technical ground truth).

8. The Sample Size for the Training Set

The document does not describe a "training set" in the context of supervised machine learning for diagnostic tasks. The AiCE (Artificial intelligence Clear Engine) is mentioned as a "Reconstruction Processing Unit," suggesting it's an AI reconstruction algorithm, not an AI diagnostic algorithm. Image reconstruction algorithms may use learned models, but the source document does not provide details on their training data.

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

Not applicable, as a "training set" in the context of a diagnostic AI algorithm is not described. If the AiCE reconstructor uses a deep learning approach, its "training" would likely involve large datasets of raw MR data and corresponding high-quality reference images (e.g., from conventional reconstruction or higher-resolution scans) to learn the mapping from raw data to reconstructed images; however, this level of detail is not provided in a 510(k) summary focused on substantial equivalence of an entire MRI system.

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Vitrea Software Package is an application package developed for use on Vitrea®, a medical diagnostic system that allows the processing, review, analysis, communication and media interchange of multi-dimensional digital images acquired from a variety of imaging devices. Vitrea Software Package has the following additional indications:

Auto MPR Brain application is a post processing software of CT brain images that is intended to align images into a standard anatomical position for review. It provides tools to reformat images parallel to a standard anatomical position.

Subtraction Viewer is a viewer application that is intended for viewing original, subtraction and fusion CT images, but such subtraction processing is not a part of this application. This application supports fusion display of original image and subtraction image generated by other applications.

The Vitrea Software Package, VSTP-002A V2.0, is a portfolio of applications software designed to be used in the Canon Medical Informatics Vitrea workstation. VSTP-002A currently includes post processing applications, Auto MPR Brain and Subtraction Viewer, which use CT image data, obtained from Canon CT Systems, to assist physicians in performing specialized measurements and analysis.

Auto MPR Brain is a software application that aligns CT brain images into a standard anatomical position for review.

Subtraction Viewer, also marketed as SCT Viewer, is a software application intended to view original, subtraction and fusion CT images. This application supports fusion display of original images and subtraction images generated by other applications and includes the ability to adjust fusion rate and ROI measurements of fusion images.

The provided text describes a 510(k) premarket notification for the "Vitrea Software Package, VSTP-002A V2.0". Based on the information provided, the acceptance criteria and study proving the device meets these criteria can be described as follows:

Acceptance Criteria and Device Performance

The acceptance criteria are implicitly derived from the "Performance Testing - Bench" section, which focuses on the "Evaluation of CE Boost Contrast-to-Noise Ratio (CNR)" for the Subtraction Viewer software.

Study Details:

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

   • Sample Size: "images of adult cases". No specific number of cases or images is provided.
   • Data Provenance: The images were "derived from a Canon CT scanner." The anatomical regions included "abdomen, brain, neck, chest, pulmonary embolism, and limb scans." The country of origin is not specified, nor is whether the data was retrospective or prospective.

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

   • The document does not mention the use of experts or the establishment of ground truth by experts for the performance testing. The evaluation described is a quantitative measurement (CNR) of the output from the "Subtraction Viewer" application.

3. Adjudication Method for the Test Set:

   • Not applicable, as no human reader evaluation or expert consensus for ground truth establishment is described. The performance test is a quantitative measurement of CNR.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC or human-in-the-loop study is mentioned. The performance evaluation focuses on the technical performance of the software (CNR measurement), not on how human readers interact with or benefit from the AI.

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

   • Yes, the described "Evaluation of CE Boost Contrast-to-Noise Ratio" is a standalone, algorithm-only performance assessment. It directly evaluates the output of the software (CE Boost images) by comparing CNR, without involving human interpretation or decision-making as part of the primary performance metric.

6. Type of Ground Truth Used:

   • The concept of "ground truth" as a clinical reference standard (e.g., pathology, outcomes data, or expert consensus on disease presence) is not directly applicable to the described performance test. The "ground truth" for the CNR evaluation is the original CE image against which the CNR of the CE Boost image is compared. The purpose of the test is to verify the software's ability to enhance contrast as intended.

7. Sample Size for the Training Set:

   • The document does not provide information about the training set size or methodology. This submission is for a "modification of a cleared device" (VSTP-002A V2.0 from VSTP-002A), and the focus of the testing section is on the performance validation of the modified features, specifically the CE Boost functionality within the Subtraction Viewer.

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

   • Not specified, as information regarding the training set and its ground truth establishment is not provided in this document.

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