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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 Lightning 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 Lightning, TSX-035A/2, v7.0 is a 16-row CT System that is intended to acquire and display cross-sectional volumes of the whole body, including the head. This system is based upon the technology and materials of previously marketed Toshiba CT systems.

The provided text describes a 510(k) submission for a Computed Tomography (CT) system (Aquilion Lightning, TSX-035A/2, V7.0), not an AI-powered device. Therefore, many of the requested criteria related to AI or algorithm performance (like effect size of human readers with AI assistance, standalone algorithm performance, training set details) are not applicable or cannot be extracted from this document.

However, I can extract information related to the device's technical performance and safety testing.

Here's the breakdown of what can be extracted based on the provided text:

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

The document does not explicitly present a table of acceptance criteria for specific performance metrics in the format you requested, nor does it give specific numerical results for these. Instead, it states that "CT image quality metrics were performed, utilizing phantoms, which validated that the subject device is substantially equivalent to the predicate device with regard to spatial resolution, CT number and contrast-to-noise ratio and noise properties." It also mentions "Representative diagnostic images, reviewed by an American Board Certified Radiologist, including head, chest, abdomen, pelvis and peripheral exams were also obtained using the subject device which demonstrates that the device produces images of diagnostic quality and; therefore, performs as intended."

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

The text mentions "Representative diagnostic images, reviewed by an American Board Certified Radiologist." It does not specify the exact sample size (number of images or patients) used for this review, nor does it state the country of origin or whether the data was retrospective or prospective.

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)

Only "an American Board Certified Radiologist" is mentioned. The exact number of radiologists is not specified, but the phrasing "an American Board Certified Radiologist" suggests it might have been one, or at least that the images were reviewed by "an" individual with that qualification, even if multiple individuals participated. The text does not specify the years of experience.

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

The document does not mention an adjudication method for the clinical image review.

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

This is not an AI device. No MRMC study and no AI assistance mentioned.

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

This is not an AI device. Not applicable.

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

For the clinical image review, the ground truth was implicitly based on expert interpretation by an American Board Certified Radiologist, determining if the images were of "diagnostic quality" and "perform as intended." For the image quality metrics, the ground truth was established through phantom testing (e.g., measuring spatial resolution, CT number, CNR, noise properties).

8. The sample size for the training set

This is not an AI device. Not applicable.

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

This is not an AI device. Not applicable.

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