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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.

Aquilion ONE (TSX-305A/3) V7.3 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 Toshiba CT systems.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Aquilion ONE (TSX-305A/3) V7.3:

1. Table of Acceptance Criteria and Reported Device Performance:

The document is a 510(k) summary for a premarket notification for a Computed Tomography X-ray System. It is not a clinical study report with specific acceptance criteria directly tied to a diagnostic performance metric (like sensitivity or specificity) of a disease-detecting AI algorithm. Instead, it demonstrates substantial equivalence to a predicate device, focusing on technical specifications and image quality for general diagnostic use.

Therefore, the "acceptance criteria" here relate to demonstrating that the new device performs acceptably for its intended use and is equivalent to the predicate. The "performance" is primarily a comparison of technical specifications and image quality metrics against the predicate.

• View rate: Maximum 2910 views/s (same)
• Detector: 896 channels x 320 rows (same)
• Pitch factor: Range 0.555 to 1.575 / 0.555 to 1.5 (very similar)
• FOV: 240/320/500mm / 180/240/320/400/500mm (subject has slightly reduced range, but still within typical diagnostic needs)
• Metal artifact reduction: SEMAR (Volume, Helical, ECG gated) / SEMAR (Volume, Helical) (subject has added ECG gated capability)
• Gantry opening size: 780 mm (same)
• All previously cleared software options are listed as "no change" in functionality, with some having "workflow improvements" (e.g., Lung Volume Analysis, surESubtraction Lung, MyoPerfusion, Dual Energy System Package, 4D Airways Analysis) which are enhancements rather than regressions.

Differences (addressed through testing or not raising new concerns):

• Gantry Rotation Speed: 0.35s (Optional max 0.275s) for subject vs. 0.275s (Standard or optional) for predicate. This indicates a minor hardware difference, likely addressed by showing image quality is maintained.
• Wedge filter types: Two types for subject vs. Three for predicate. This is a minor design change.
• X-ray tube voltage/current: Max 72kW (Optional Max 90kW) for subject vs. Max 90kW (for one model) or Max 72kW (for others) for predicate. Comparable.
• Image reconstruction time: Up to 80 images/s for subject vs. Up to 50 images/s for predicate. Improvement in subject device.
• Helical reconstruction method: 20 rows or more: TCOT+ for subject vs. 16 rows or more: TCOT+ for predicate. Improvement in subject device (more rows).
• Patient Couch Type and related dimensions/weights: Various configurations/differences between subject and predicate models, indicating design variations but within expected functional range.
• Gantry tilt angle: ±30° for subject vs. ±22° for predicate. Improvement in subject device.
• Minimum area for installation: Smaller for subject (27m² vs 37.2m²). Improvement in subject device.
• Area finder: Optional for subject vs. NA for predicate. New feature on subject device. |
  | Image Quality | Image quality metrics (spatial resolution, CT number magnitude/uniformity, noise properties, low contrast detectability/CNR performance) should meet established specifications and be comparable to the predicate device. Images obtained should be of diagnostic quality. | CT image quality metrics performed using phantoms demonstrated that the subject device is substantially equivalent to the predicate device with regard to: spatial resolution, CT number magnitude/uniformity, noise properties, and low contrast detectability/CNR performance. Representative diagnostic images (head, chest, abdomen/pelvis, extremity, cardiac) were also reviewed and demonstrated diagnostic quality. |
  | Safety and Standards Compliance | The device must be designed and manufactured under Quality System Regulations (21 CFR 820, ISO 13485) and conform to applicable performance standards for ionizing radiation-emitting products (21 CFR, Subchapter J, Part 1020). It must also comply with various IEC, NEMA, and other relevant standards. | The device is designed and manufactured under QSR and ISO 13485. It conforms to applicable performance standards for Ionizing Radiation Emitting Products [21 CFR, Subchapter J, Part 1020] and numerous international standards including IEC60601-1 series, IEC60601-2 series, IEC60825-1, IEC62304, IEC62366, NEMA PS 3.1-3.18, NEMA XR-25 and NEMA XR-26. |
  | Software Validation | Software documentation must comply with FDA guidance for a Moderate Level of Concern, and validation testing should be successfully completed. | Software Documentation for a Moderate Level of Concern was included. Successful completion of software validation is cited in the conclusion. |
  | Risk Management | Risk analysis should be conducted. | Risk analysis was conducted. |

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

• Test Set Description: The "test set" for this submission primarily consists of:
  • Phantoms: Used for evaluating CT image quality metrics (spatial resolution, CT number, noise, low contrast detectability). The number and specific types of phantoms are not explicitly stated but are typically standard phantoms used in CT performance testing.
  • Representative Diagnostic Images: Clinical images covering various body regions (head, chest, abdomen/pelvis, extremity, cardiac). The number of cases/patients is not specified.
• Data Provenance: The document does not explicitly state the country of origin for the diagnostic images. Given Toshiba Medical Systems Corporation is based in Japan and Toshiba America Medical Systems, Inc. is in the US, the data could originate from either region or a combination. The document also does not specify if the data was retrospective or prospective. However, for a 510(k) clearance based on substantial equivalence, particularly for a hardware/software update to a CT scanner, diagnostic images are often retrospectively collected or acquired as part of internal validation.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

• Number of Experts: One (1) expert is explicitly mentioned.
• Qualifications of Experts: An "American Board Certified Radiologist." No specific years of experience are stated. This expert reviewed the representative diagnostic images to confirm diagnostic quality.

4. Adjudication Method for the Test Set:

• The document describes a single American Board Certified Radiologist reviewing images to confirm diagnostic quality. This indicates no formal adjudication method involving multiple readers (like 2+1 or 3+1) was used for this specific part of the evaluation. The assessment of image quality from phantoms would not typically involve expert adjudication.

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

• No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done. This document is for a general-purpose CT scanner system, not an AI-specific diagnostic tool that assists human readers. Therefore, there is no mention of an effect size for human reader improvement with or without AI assistance.

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

• No, a standalone performance study in the context of an AI algorithm was not done. The Aquilion ONE (TSX-305A/3) V7.3 is a complete CT system where the "algorithm" refers to the image reconstruction and processing capabilities, which are inherent to the device's function. The study validates the overall system's ability to produce diagnostic images, not a separate AI algorithm's diagnostic accuracy. The performance is assessed on the system output.

7. The Type of Ground Truth Used:

• For the phantom studies, the "ground truth" is typically known physical properties of the phantoms (e.g., known dimensions, densities, contrast levels).
• For the representative diagnostic images, the "ground truth" for confirming "diagnostic quality" is based on the expert opinion/consensus of an American Board Certified Radiologist. This is a form of expert consensus, albeit from a single expert in this stated context. There is no mention of pathology or outcomes data being used as ground truth for this submission.

8. The Sample Size for the Training Set:

• The document does not specify a separate "training set" sample size. This submission is for a medical imaging device (CT scanner) rather than a deep learning AI algorithm that undergoes distinct training. The underlying algorithms for image reconstruction and processing (e.g., TCOT+, SEMAR) are developed and refined through engineering and iterative testing, but not typically in the same "training set" paradigm as AI for diagnostic interpretation. The software validation is mentioned, which refers to standard software development lifecycle testing.

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

• As a "training set" in the context of AI development is not explicitly mentioned as relevant to this submission, the establishment of ground truth for a training set is not applicable/described. The "ground truth" during the development of a CT scanner's image reconstruction algorithms would typically involve engineering specifications, physical models, and potentially early clinical data used for empirical tuning and validation, but not a formally labeled training set in the AI sense.

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This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head.

The Aquilion Prime 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.

The Aquilion PRIME TSX-303A/A and /B, v6.00 are 80-row CT Systems and the TSX-303A/F, v6.00 is a 40-row CT system that is intended to produce axial scans of the whole body to include the head. These systems are based upon the technology and materials of previously marketed Toshiba CT systems.

This document is a 510(k) premarket notification for a Computed Tomography (CT) system, the Aquilion PRIME, v6.00. As such, it focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study with specific acceptance criteria and performance metrics in the way one might expect for a novel AI-powered diagnostic device.

Therefore, the information regarding "acceptance criteria" and the "study that proves the device meets the acceptance criteria" is framed within the context of demonstrating equivalence and safety/effectiveness for a hardware/software update to an existing CT system, rather than a standalone performance study with clinical endpoints.

Here's an attempt to extract the closest available information based on your request, acknowledging that the format and detail for a conventional "acceptance criteria" study are not fully present in this type of submission.

1. A table of acceptance criteria and the reported device performance

Based on the document, the "acceptance criteria" are implied by demonstrating substantial equivalence to the predicate device and meeting regulatory standards for CT systems. The "reported device performance" is described in terms of improved imaging properties and diagnostic quality.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: Not explicitly stated as a number of patients or cases. The document mentions "representative diagnostic images" but does not quantify them.
• Data Provenance: Not specified. It's likely that the "representative clinical images" were obtained during internal testing or pilot sites, but no details on country or retrospective/prospective nature are provided.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

• Number of Experts: "an American Board Certified Radiologist" (singular).
• Qualifications of Experts: "American Board Certified Radiologist." No specific experience level (e.g., 10 years) is mentioned.

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

• Adjudication Method: "Reviewed by an American Board Certified Radiologist." This implies a single reader review, so no adjudication method (like 2+1 or 3+1) was used.

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

• MRMC Study: No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This submission is for a CT system itself, not an AI-assisted diagnostic tool designed to improve human reader performance. Its purpose is to demonstrate the fundamental image quality and safety of the CT scanner.

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

• This is not applicable in the context of this submission. The device is a CT scanner, which inherently produces images for human interpretation. The "software options" mentioned (SEMAR, SURESubtraction Ortho, Dual Energy System Package) are image processing algorithms that enhance the raw CT data, but the "performance" as described (image quality metrics, diagnostic quality) still relates to the final image presented for a human in the loop. There is no "algorithm only" performance study in the sense of an automated diagnostic algorithm.

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

• The "ground truth" for evaluating image quality appears to be based on:
  • Phantom measurements: For spatial resolution, CT number, contrast-to-noise ratio, and uniformity performance.
  • Expert opinion: The "American Board Certified Radiologist" reviewing representative diagnostic images for diagnostic quality. This functions as the human expert assessment indicating the images are fit for interpretation.

8. The sample size for the training set

• Not applicable/provided. This document describes a new version of an existing CT scanner, not a novel machine learning algorithm that requires a separate training set. The "software options" mentioned were previously cleared and their development (including any training data if applicable) would have been part of their original 510(k) submissions.

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

• Not applicable. See point 8.

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