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1717G Digital Flat Panel X-Ray Detector is indical 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.

1717G is a digital solid state X-ray detector that is based on flat-panel technology. 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 study details for the Rayence 1717G Digital Flat Panel X-ray Detector, based on the provided 510(k) summary:

Acceptance Criteria and Device Performance

The acceptance criteria for the 1717G device are fundamentally based on demonstrating substantial equivalence to its predicate device, the 1717SGC, in terms of image quality and safety. The study focuses on comparing the performance metrics and clinical utility of both devices.

Study Details

2. Sample size used for the test set and the data provenance

• Sample Size for Test Set: Not explicitly stated as a number of images or cases. The document mentions "sample radiographs of similar age groups and anatomical structures."
• Data Provenance: Not explicitly stated. The study involved a "licensed US radiologist" and "radiographs of similar age groups and anatomical structures," suggesting the data was likely obtained from human subjects within a clinical or test setting, but the country of origin is not specified. It appears to be prospective data collected for the purpose of the comparison, as "clinical images are taken from both devices" for review.

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

• Number of Experts: One.
• Qualifications of Experts: A "licensed US radiologist." No specific years of experience are mentioned.

4. Adjudication method for the test set

• Adjudication Method: Not applicable in the typical sense of multiple readers reaching a consensus. A single licensed US radiologist performed a comparative review, evaluating and comparing images from both devices. There was no explicit adjudication process described for conflicting opinions, as there was only one reviewer.

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

• MRMC Study: No, a multi-reader, multi-case (MRMC) comparative effectiveness study was not explicitly stated or conducted as described here. The study involved a single radiologist comparing images from two devices to assess substantial equivalence, not to evaluate human reader improvement with or without AI assistance. This device is an X-ray detector, not an AI software.

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

• Standalone Study: Yes, a form of "standalone" (algorithm/device-only) performance testing was done in the form of non-clinical tests:
  • MTF (Modulation Transfer Function)
  • DQE (Detective Quantum Efficiency)
  • NPS (Noise Power Spectrum)
    These tests directly measure the technical performance characteristics of the detector itself, independent of human interpretation.

7. The type of ground truth used

• Type of Ground Truth: The ground truth for the technical performance (MTF, DQE, NPS) is derived from the standardized comparison against the predicate device using established metrics (IEC 62220-1). For the clinical image quality, the ground truth is established by expert opinion/consensus from a licensed US radiologist comparing the visual quality of images (e.g., spatial resolution, soft tissue contrast) from both devices. The goal was to prove substantial equivalence, not to diagnose specific conditions against a definitive pathology or outcomes ground truth.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable. This device is a digital X-ray detector (hardware), not an AI algorithm that requires a training set in the machine learning sense. The "training" or development involved engineering and design, with the predicate device serving as a reference.

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

• How Ground Truth for Training Set was Established: Not applicable, as this is a hardware device and not an AI algorithm requiring a training set. The "ground truth" for its development would be engineering specifications and performance targets based on the predicate device and industry standards.

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