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Digital Flat Panel X-Ray Detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. Not to be used for mammography.

1417WCE, 1417WCE-HR, 1417WCE-HS, 1417WCE-GF X-ray detectors, are wired/wireless digital solid state X-ray detectors that are based on flat panel technology. The wireless LAN (IEEE 802.11 n/ac) communication signals images captured to the system and improves the user operability through high speed processing. These radiographic image detectors are processing unit consist of a scintillator coupled to an a-Si TFT sensor. These devices need to be integrated with a static radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis.

The revised 510k Summary specified that 1417WCE, 1417WCE-HR, 1417WCE-HS, 1417WCE-GF includes the software (firmware) of MODERATE level of concern. It's the same Image Acquisition and Operating Software used for the predictive device is used but modified to include additional detector models in comparison with the predicate device.

The RAW files can be further processed as DICOM compatible image files by separate consol SW (K190866, XmaruView V1 / Rayence Co.,Ltd) for a radiographic diagnosis and analysis. The imaging software XMaru View V1 is not part of the subject device.

1417WCE is the basic model. 1417WCE-HR is identical with the basic model except for the pixel pitch size. 417WCE-HS is identical with the basic model except for the case color. 1417WCE-GF is identical with the basic model except for the case color and the pixel pitch size. The differences are not safety related.

The provided text is a 510(k) summary for Rayence Co., Ltd.'s X-ray detectors (1417WCE, 1417WCE-HR, 1417WCE-HS, 1417WCE-GF). It describes the substantial equivalence of these new devices to a previously cleared predicate device (1417WCC, K171418), rather than proving the device meets a specific set of new clinical acceptance criteria through a clinical study.

The core of this submission is non-clinical performance testing to demonstrate that the new devices perform equivalently or better than the predicate, not a clinical study proving diagnostic accuracy or human performance improvement.

Therefore, many of the requested elements for a clinical study (like sample size for test sets, data provenance, number of experts, adjudication methods, MRMC studies, standalone performance with ground truth methods, and training set details) are not applicable or not explicitly stated in this type of submission.

However, I can extract information related to the acceptance criteria implicitly used for substantial equivalence and the non-clinical study details that support the performance claims.

Here's an analysis based on the provided document:

Acceptance Criteria and Reported Device Performance (Implicit for Substantial Equivalence)

The acceptance criteria for this 510(k) submission are implicitly tied to demonstrating substantial equivalence to the predicate device (1417WCC). This means showing that the new devices are as safe and effective as the predicate. The "performance" in this context refers to technical imaging characteristics rather than diagnostic accuracy in a clinical setting.

Implicit Acceptance Criteria and Reported Device Performance:

Study Details (Non-Clinical Performance Testing for Substantial Equivalence)

The provided document describes non-clinical performance testing and a qualitative image review to support substantial equivalence, rather than a full-scale clinical trial with human subjects and diagnostic outcomes.

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: Not specified in terms of number of images or patients for the qualitative review. The document mentions comparing "plain radiographic images" from the new devices and the predicate.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The comparison indicates the images were "taken with" the devices and from "a similar patient," implying they were internally generated or acquired for comparison. It was a retrospective comparison of existing image types (though not necessarily existing patient data in a large dataset).

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

   • Not Applicable in the traditional sense of diagnostic ground truth. For the qualitative image review, the document states: "After comparing a broad review of plain radiographic images... images obtained equivalent quality..." This implies a subjective assessment, likely by internal experts, but the number and qualifications are not specified. This is a technical comparison for substantial equivalence, not a diagnostic accuracy study requiring independent expert ground truth for clinical endpoints.

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

   • None described. The qualitative image review mentions a "broad review," implying an assessment was made, but no formal adjudication process (like 2+1 reader agreement) is detailed.

4. 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, an MRMC comparative effectiveness study was NOT done. This submission is for digital X-ray detectors themselves, not an AI-powered diagnostic assistance tool. Therefore, a study on human reader improvement with AI assistance is not applicable.

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

   • Yes, in the sense of technical performance testing. The device's technical performance (MTF, DQE, NPS) was evaluated "algorithm only" (as in, device output without human interpretation in the loop) and compared to the predicate device, following international standards (IEC 62220-1). However, this is not a "standalone performance" study measuring diagnostic accuracy.

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

   • Primarily
     technical specifications and qualitative comparison; no traditional diagnostic ground truth. The "ground truth" for this submission are the measured physical properties of the detectors (e.g., MTF, DQE values determined by standardized phantoms) and the subjective assessment of image quality against the predicate. This is sufficient for demonstrating substantial equivalence for a medical imaging device (detector), not for an AI algorithm that provides a diagnostic output.

7. The sample size for the training set:

   • Not Applicable. This submission is for a physical X-ray detector, not an AI or machine learning algorithm that requires a "training set."

8. How the ground truth for the training set was established:

   • Not Applicable. As there is no training set for this device type, no ground truth needed to be established for it.

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