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1417WCC 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.

1417WCC is a wired/wireless digital solid state X-ray detector that is based on flat-panel technology. The wireless LAN(IEEE 802.11a/g/n) communication signals images captured to the system and improves the user operability through high-speed processing. This radiographic image detector and processing unit consists of a scintillator coupled to an a-Si TFT sensor. This device needs to be integrated with a radiographic imaging system. It can be utilized to capture and digitalize X-ray images for radiographic diagnosis The RAW files can be further processed as DICOM compatible image files by separate console SW (not part of this 510k submission) for a radiographic diagnosis and analysis.

Here's a breakdown of the acceptance criteria and the study details for the Rayence 1417WCC Digital Flat Panel X-ray Detector, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the 1417WCC are based on demonstrating substantial equivalency to its predicate device, the 1417WCA. This is primarily achieved by showing comparable or superior performance in key imaging metrics and overall image quality.

2. Sample Size for the Test Set and Data Provenance

• Sample Size: The document does not explicitly state a specific number for the test set (clinical images). It mentions "sample radiographs of similar age groups and anatomical structures."
• Data Provenance: The clinical images were taken to demonstrate substantial equivalency between the 1417WCC and 1417WCA. The text does not specify the country of origin of the data, but it was reviewed by a "licensed US radiologist," implying the images might have been collected or evaluated in the US (or at least by a US-qualified professional). The study appears to be a prospective comparative study looking at new images generated by both devices for comparison.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

• Number of Experts: One licensed US radiologist.
• Qualifications: "licensed US radiologist." No further details on years of experience or sub-specialization are provided.

4. Adjudication Method for the Test Set

The document does not explicitly state a formal adjudication method (e.g., 2+1, 3+1). It indicates that the images were "reviewed by a licensed US radiologist to render an expert opinion." This suggests a single expert review rather than a consensus-based adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done

No, an MRMC comparative effectiveness study was not conducted in this submission. The study involved a single expert reviewer comparing images from the test device (1417WCC) and the predicate device (1417WCA).

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Yes, a standalone performance assessment was done for the device's technical image quality parameters:

• MTF, DQE, and NPS tests were performed on both the 1417WCC and 1417WCA using "identical test equipment and same analysis method described by IEC 62220-1." These are objective, algorithm-only (device-only) measurements of physical imaging performance.

7. The Type of Ground Truth Used

• Non-clinical (Technical Performance): Objective measurements based on established standards (IEC 62220-1) for MTF, DQE, and NPS.
• Clinical (Subjective Evaluation): Expert opinion from a licensed US radiologist, serving as the ground truth for subjective image quality assessment and comparison. This would fall under expert consensus if there were multiple experts, but here it's a single expert's opinion.

8. The Sample Size for the Training Set

The document does not provide information on a training set. This device is a digital X-ray detector, which is hardware that captures images. It's not an AI/algorithm-based diagnostic device that typically requires a large training set of images with established ground truth for a machine learning model. The evaluation focuses on the performance characteristics of the hardware itself.

9. How the Ground Truth for the Training Set Was Established

As no training set is mentioned or applicable for this type of device (a digital X-ray detector hardware), this question is not relevant to the provided information.

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