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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 imaqe whole orqans in a sinqle 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 orqan. These volume sets can be used to perform specialized studies, usinq indicated software/hardware, of the whole organ by a trained and qualified physician.

FIRST 2.0 is an iterative reconstruction alqorithm intended to reduce exposure dose and improve high contrast spatial resolution for abdomen, pelvis, chest, cardiac and extremities applications.

Aquilion ONE Vision with FIRST 2.0 (CCRS-001B) V7.4 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. In addition, the subject device incorporates the latest iterative reconstruction technology, FIRST 2.0, intended to reduce exposure dose while maintaining and/or improving image quality.

The provided text describes a 510(k) premarket notification for a Computed Tomography (CT) system named Aquilion ONE Vision with FIRST 2.0 (CCRS-001B) V7.4. The documentation focuses on demonstrating substantial equivalence to a predicate device (Aquilion ONE Vision with FIRST 1.0) by updating an iterative reconstruction algorithm (FIRST 2.0).

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

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

The document does not explicitly state "acceptance criteria" in a numerical or pass/fail table format for clinical performance. Instead, it describes performance in terms of improvements or equivalency compared to the predicate device and filtered back projection (FBP).

• Contrast-to-noise ratio
• CT number accuracy
• Uniformity
• Slice sensitivity profile
• Modulation transfer function
• Line pair gauge
• Low contrast detectability
• Standard deviation of noise
• Noise power spectra. |
  | Diagnostic Quality | Representative diagnostic images reviewed by an American Board Certified Radiologist demonstrated that the device produces images of diagnostic quality and performs as intended. |

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

• Test Set Sample Size: The document mentions "Representative diagnostic images... including chest, abdomen and pelvis, extremity and cardiac exams." However, it does not specify the number of images or cases used for this review.
• Data Provenance: The document implies the data was collected from the device itself ("obtained using the subject device"). There is no information about the country of origin of the data or whether it was retrospective or prospective. Given it's a premarket submission for a new version of an existing device, it's likely part of internal validation testing.

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

• Number of Experts: "an American Board Certified Radiologist" – indicates one expert was used.
• Qualifications: "American Board Certified Radiologist" – this implies a board certification, which generally requires specific training, residency, and passing board examinations, demonstrating a certain level of expertise in radiology. The document does not specify the years of experience of this radiologist.

4. Adjudication method for the test set

• Since only one radiologist was used for the diagnostic image review, an adjudication method (like 2+1 or 3+1) was not applicable/performed. The single expert's review served as the assessment.

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

• No MRMC study was mentioned or performed as part of this submission. The evaluation was primarily focused on technical image quality metrics and a single radiologist's review of diagnostic image quality, not the impact on human reader performance with or without AI assistance. The "AI" here (FIRST 2.0) is an iterative reconstruction algorithm, which enhances image quality/dose reduction, rather than an AI-driven diagnostic aid that would typically warrant a comparative reader study.

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

• Yes, an algorithm-only (standalone) performance evaluation was done in terms of quantitative image quality metrics (e.g., contrast-to-noise ratio, spatial resolution, dose reduction claims) using phantoms.
• "A model observer evaluation" directly assessed the algorithm's performance in achieving "equivalent low contrast detectability" with reduced dose, which is a standalone assessment of the algorithm's output.

7. The type of ground truth used

• For quantitative image quality metrics and dose reduction claims: Phantoms (e.g., MITA-FDA phantom) were used to provide a known, controlled ground truth.
• For diagnostic image quality review: The "ground truth" was established by the expert opinion of an American Board Certified Radiologist who reviewed representative images for "diagnostic quality." This is essentially expert consensus (albeit from a single expert).

8. The sample size for the training set

• The document does not provide any information about the sample size used for training the FIRST 2.0 iterative reconstruction algorithm. This information is typically proprietary to the manufacturer and not required in this level of 510(k) summary.

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 (if any, as iterative reconstruction algorithms might use phantoms or specific patient data for training their models without needing human-labeled "ground truth" for diagnosis) was established. This detail is also not typically shared in a 510(k) summary.

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