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CombiDiagnost R90 is a multi-functional general R/F system. It is suitable for all routine radiography and fluoroscopy exams, including specialist areas like angiography or pediatric work, excluding mammography.

The CombiDiagnost R90 is a multi-functional remote controlled fluoroscopy system in combination with a high-end digital radiography system consisting of a floor-mounted tilt- and heightadjustable patient support and a scan unit consisting of a tube and a flat panel detector, Pixium FE 4343F. The tabletop can be moved by a motor in the lateral direction and can be tilted +/- 90 degrees. The scan unit tilts with the table and can be moved in the longitudinal direction, relative to the table and to the patient. The fully integrated system is provided with a touch screen console, glass or metal x-ray tube(s) with collimator and high resolution displays. As a fully integrated system, the proposed CombiDiagnost R90 can be configured with a Philips generator, the flat panel detector Pixium FE 4343F, and the Philips Dynamic Eleva Image Chain acquisitionstation also provided with the currently marketed and reference device, Eleva Workspot with SkyFlow (Eleva Workspot). The proposed CombiDiagnost R90 uses the same workflow from the currently marketed and reference device, Eleva Workspot with SkyFlow. The only modification to the Eleva Workspot is integration with the Pixium FE 4343F detector.

The provided text is a 510(k) summary for the Philips CombiDiagnost R90, an X-ray system. This document focuses on demonstrating substantial equivalence to a predicate device, rather than providing a detailed study proving the device meets specific acceptance criteria in the manner one might expect for a diagnostic AI algorithm.

Therefore, much of the requested information regarding acceptance criteria for a diagnostic performance study, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or standalone performance of an AI algorithm is not applicable to this type of submission. This document describes a medical device, not a diagnostic AI.

However, I can extract the information related to the device's technical specifications and the non-clinical testing performed to show its safety and effectiveness.

Here's the breakdown of the available information:

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

The document does not provide a specific table of quantitative acceptance criteria for diagnostic performance and corresponding reported performance values for the CombiDiagnost R90's imaging capabilities (e.g., sensitivity, specificity for a particular pathology). Instead, it presents a comparison of technological characteristics between the proposed device and its predicate, along with a statement that non-clinical tests met acceptance criteria.

Technological Characteristics Comparison (Proposed Device vs. Predicate Device)

The study (non-clinical verification and validation tests as well as image quality testing) "demonstrate[d] that the proposed CombiDiagnost R90 meets the acceptance criteria and is adequate for its intended use." The acceptance criteria for these non-clinical tests are defined by the referenced international and FDA-recognized consensus standards.

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

This information is not provided in the document as it is a 510(k) submission for an X-ray system, not a diagnostic AI algorithm. The "test set" here refers to the physical device undergoing non-clinical technical testing, not a dataset of patient images.

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)

This information is not applicable as the document describes non-clinical technical testing of an X-ray system, not a diagnostic study requiring expert ground truth for image interpretation.

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

This information is not applicable for the same reasons as #3.

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

An MRMC study was not done. This type of study is relevant for evaluating the impact of AI on human reader performance, which is not the purpose of this 510(k) submission for an X-ray system.

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

A standalone performance study of a diagnostic algorithm was not done. This document concerns an X-ray imaging system.

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

For the non-clinical testing of the CombiDiagnost R90, the "ground truth" would be established by engineering specifications and validated measurement equipment according to the referenced standards (e.g., IEC 62220-1 for detective quantum efficiency, IEC 60601 series for safety and performance). This is a technical ground truth, not a clinical one based on patient outcomes or expert reads.

8. The sample size for the training set

This information is not applicable as the device is an X-ray system, not an AI algorithm requiring a training set of images.

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

This information is not applicable for the same reasons as #8.

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