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The Silhouette VR is indicated for use in generating radiographic images of human anatomy in all general purpose diagnostic procedures. This device is not intended for mammographic applications.

The Silhouette VR consists of an integrated table and tube stand, under-the-table generator and power distribution unit, wall stand, dual focal spot x-ray tube and operator's console.

The provided text is a 510(K) Summary of Safety and Effectiveness for the "Silhouette VR" Stationary X-ray System. This document focuses on demonstrating substantial equivalence to pre-existing devices, rather than presenting a study proving the device meets specific acceptance criteria in terms of performance metrics like sensitivity, specificity, or image quality, as would be expected for an AI/software device.

Therefore, many of the requested categories about acceptance criteria, study details, and AI-specific metrics cannot be answered directly from the provided text. The document is essentially a regulatory submission for a hardware medical device (an X-ray system) asserting its conformance to existing standards and its equivalence to other marketed devices.

Here's an attempt to extract what is available, with explicit notes on what is not provided:

1. A table of acceptance criteria and the reported device performance
   • Acceptance Criteria (Implied): Conformance to applicable sections of 21 CFR 1020.30 and 1020.31 (for X-ray equipment), UL 2601-1 (which includes IEC 601-1 and UL 187), and IEC 601-1-2 for EMC. The primary "acceptance criteria" for this type of submission is substantial equivalence to a predicate device.
   • Reported Device Performance: The document does not provide specific performance metrics (e.g., spatial resolution, contrast resolution, tube output, image quality scores) that would typically be reported for testing against acceptance criteria for a new, innovative device. Instead, it asserts conformance to safety and performance standards.

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

   • Not provided. This device is a hardware X-ray system, not an AI or imaging analysis software that operates on a dataset. The "test set" would typically refer to engineering tests and compliance measurements against standards, not a patient image dataset.

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)

   • Not applicable/Not provided. This information is relevant for AI or diagnostic software. For an X-ray machine, ground truth relates to physical measurements and engineering specifications, not expert consensus on images.

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

   • Not applicable/Not provided. This is specific to diagnostic performance studies, typically involving human readers or AI algorithms.

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. This device is an X-ray system, not an AI-assisted diagnostic tool.

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

   • No. This device is an X-ray system, not an algorithm.

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

   • Not applicable/Not provided. For a hardware device, "ground truth" would be against physical standards and engineering benchmarks.

8. The sample size for the training set

   • Not applicable/Not provided. This device does not use a "training set" in the context of machine learning.

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

   • Not applicable/Not provided. This device does not use a "training set" in the context of machine learning.

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