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Venu1748V and DRX-LC, as a major imaging component, are supplied to the manufacturers of medical diagnostic X-ray photography systems, and used in conjunction with the medical diagnostic X-ray photography system to image the object to be checked. They are capable of outputting the acquired static images to a processing device after acquisition.

Venu1748V and DRX-LC digital flat panel detector (Hereinafter referred to as Venu1748V and DRX-LC) are digital large-sized X-ray flat panel detector (FPD) with wireless function based on amorphous silicon (a-Si) thin film transistor (TFT) technology. Two models Venu1748V and DRX-LC are totally same except for the model name, trade mark, artwork of the protection film. They using cesium iodide (CsI) scintillator, and employ a 3064×8696 active pixel matrix with a pixel size of 139 u m, providing high-quality radiographic images. Supporting high-speed wireless communication, the equipment can be powered by internal rechargeable battery packs or/and external power charger, making it more flexible and easy to integrate and operate. iRay SDK(include iDetector) is intend to supply API interface for DR system manufacturers. DR system manufacturer control the detector by SDK interface. SDK is not intended to be used directly by other users beside DR system manufacturers. iDetector is a tool software based on iRay FPD(Flat Panel Detector) and SDK(Software Development Kit). It can be used for detector configuration, image acquisition, and calibration. So that users can evaluate the performance of iRay detectors at the first time. Also, iDetector can be used as a demonstration program to learn the process controlling and functionality of iRay Detectors and do assessment at user application developing time. This software is moderate level of concern. iDetector does not support image processing after collection.

This document describes the premarket notification (510(k)) for the iRay Imaging Technology (Haining) Limited Digital Flat Panel Detector (models Venu1748V and DRX-LC).

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

The document does not explicitly state "acceptance criteria" but rather presents a comparison of technological characteristics between the proposed device and a predicate device (K220536). The proposed device meets or improves upon the characteristics of the predicate device.

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

The document focuses on non-clinical performance testing and comparative analysis against a predicate device. There is no mention of a "test set" in the context of clinical data or human-interpreted image sets for evaluating device diagnostic performance. The studies are non-clinical, evaluating technical specifications of the device itself.

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)

Not applicable. The reported studies are non-clinical and focus on characterizing device performance parameters (e.g., MTF, DQE, uniformity) rather than diagnostic accuracy based on expert interpretation of images. Therefore, no experts were used to establish ground truth for a diagnostic test set.

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

Not applicable. As no clinical or human-interpreted test set was used, no adjudication method was employed.

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

Not applicable. This submission is for a Digital Flat Panel Detector, which is an imaging component. It is not an AI-powered diagnostic device, and therefore, no MRMC study or evaluation of human reader improvement with AI assistance was performed.

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

Not applicable. The device is a Digital Flat Panel Detector, a hardware component for X-ray imaging. Performance is evaluated based on its technical specifications (e.g., MTF, DQE, SNR, uniformity) through non-clinical studies. There is no "algorithm only" performance assessment in the context of diagnostic interpretation.

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

The "ground truth" for the non-clinical studies relies on established scientific and engineering principles for measuring physical parameters of X-ray detectors (e.g., using phantoms, standardized test setups, and calibrated equipment to measure MTF, DQE, SNR, etc.). It does not involve expert consensus, pathology, or outcomes data in a clinical sense.

8. The sample size for the training set

Not applicable. This device is a hardware component and does not utilize a "training set" in the context of machine learning or AI algorithms.

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

Not applicable. As there is no training set for AI, no ground truth needed to be established for it.

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