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As a part of a radiographic system, the SkyPlate Detector for Philips Radiography/Fluoroscopy Systems is intended to acquire, process, store, display and export digital radiographic images. The SkyPlate Detector for Philips Radiography/ Fluoroscopy Systems is suitable for all routine radiography exams, including specialist areas like intensive care, trauma, or pediatric work, excluding mammography.

The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems is a Solid State X-ray Imaging Device that converts x-ray patterns into electrical signals. The signals are converted into visible images for use in medical diagnosis. A cesium iodide scintillator absorbs the input x-ray photons in the detector. The cesium iodide scintillator in turn emits visible spectrum photons that illuminate an array of photodetectors that create an electrical charge representation of the x-ray input. A matrix scan of the array converts the integrated charges into a modulated electrical signal.

The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems is optionally installed and is intended to be integrated into an x-ray system, where it constitutes an x-ray receptor for direct radiography x-ray imaging. It is electrically powered by and connected with the x-ray system. The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems is connected to the Philips Eleva Workspot with SkyFlow (cleared via K153318) to create a complete x-ray imaging chain, and is intended to be used for radiography in Philips Radiography/Fluoroscopy systems, such as the CombiDiagnost R90 (cleared via K163210). The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems, is used to acquire diagnostic radiographic images during radiographic procedures.

This document is a 510(k) premarket notification for the SkyPlate Detector for Philips Radiography/Fluoroscopy Systems. It outlines the device's characteristics, intended use, and claims of substantial equivalence to a predicate device.

Here's an analysis of the provided text in relation to your questions:

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

The document does not provide a specific table of quantitative acceptance criteria for device performance. Instead, it states that the device:

• "Complies with the aforementioned international and FDA-recognized consensus standards and device specific guidance documents."
• "Meets the acceptance criteria and is adequate for its intended use."
• "is as safe, as effective, and performs as well or better than the predicate device."
• "provides images of equivalent diagnostic capability."

The full list of standards mentioned are:

• ISO 14971: Medical Devices - Application of risk management to medical devices
• NEMA PS 3.1-3.20 Digital Imaging and Communication in Medicine (DICOM) Set
• AAMI ANSI IEC 62304:2006 Medical Device Software - Software lifecycle processes
• IEC 62220-1: Medical electrical equipment, Characteristics of digital x-ray imaging devices
• IEC 62220-1-1:2015: Medical electrical equipment, Characteristics of digital x-ray imaging devices – Part 1-1: Determination of the detective quantum efficiency – Detectors used in radiographic imaging
• IEC 60601-1: Medical electrical equipment – Part 1: General requirements for basic safety and essential performance (IEC 60601-1:2005 + A1:2012, MOD)
• IEC 60601-1-2: Medical electrical equipment. General requirements for basic safety and essential performance. Collateral standard: Electromagnetic compatibility requirements and tests
• IEC 60601-1-3: Medical electrical equipment. General requirements for basic safety and essential performance. Collateral standard: Radiation in diagnostic X-ray equipment
• IEC 60601-2-54: Medical electrical equipment. Particular requirements for basic safety and essential performance of X-ray equipment for radiography and radioscopy
• Guidance for the Submission of 510(k)s for Solid State C X-ray Imaging Device", issued September 1, 2016
• "Guidance for the Content of Premarket Submissions for O software contained in Medical Device", issued May 11, 2005
• Pediatric information for X-Ray Imaging Device Premarket Notifications, Draft, issued May 10, 2012

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

The document explicitly states: "The SkyPlate Detector for Philips Radiography/Fluoroscopy Systems did not require a clinical study since substantial equivalence to the currently marketed and predicate device was demonstrated with the following attributes: Design features; Indication for use; Fundamental scientific technology; Non-clinical performance testing including validation; and Safety and effectiveness." Therefore, no clinical test set was used for this 510(k) submission, and consequently, no clinical sample size or data provenance is 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)

Since no clinical study was conducted and no clinical test set was used, no experts were used to establish ground truth for a test set.

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

As no clinical study was conducted and no clinical test set was used, no adjudication method was applied.

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

The document does not describe any MRMC comparative effectiveness study. This device is an X-ray detector, not an AI-assisted diagnostic tool.

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

This refers to a standalone performance of an algorithm. The SkyPlate Detector is an X-ray imaging device, not an algorithm. Therefore, this question is not applicable to the device described. The submission relies on "non-clinical performance testing including validation" for the physical device.

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

For the non-clinical performance testing, the ground truth would be established by engineering and laboratory measurements demonstrating compliance with the specified international and FDA-recognized consensus standards. This would involve physical measurements of detector characteristics (e.g., image resolution, detective quantum efficiency, electrical signals, safety parameters) against defined benchmarks within those standards, rather than clinical ground truth from patient data.

8. The sample size for the training set

This question typically applies to machine learning or AI models. Since the device is a physical X-ray detector and not an AI algorithm, there is no concept of a "training set" in the context of this submission. The validation was based on non-clinical performance and engineering tests.

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

As explained above, this question is not applicable as there is no training set for this type of device.

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