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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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The Venul 748V flat panel detector is provided as an imaging component to the system manufacturer. It is mainly used in long bones, spine and other inspection fields. After collecting static imaged data is output to the processing equipment.

This device is suitable for providing radiography imaging for adult via DR system. The remaining notes depend on the final DR system.

It is not intended for mammography, dental applications, neonatal and fluoroscopy.

Digital flat panel detector is a cassette-size wired X-ray flat panel detector based on amorphous silicon thin-film transistor technologies. It was developed to provide X -Ray image, which contains an active matrix of 3064×8696 with 139um pixel pitch. Detector's scintillator is CsI(Cesium iodide). The biggest feature of Venu1748V is that it supports imaging of large-scale objects, including long bones and complete spine detection.

This document is a 510(k) Pre-Market Notification from iRay Technology Taicang Ltd. to the FDA for their Digital Flat Panel Detector, model Venu1748V. It outlines the device's technical characteristics and compares them to a predicate device.

Analysis of Acceptance Criteria and Study Details:

The provided document does not describe a clinical study or a multi-reader, multi-case (MRMC) study to prove the device meets acceptance criteria. Instead, it relies on non-clinical studies (bench testing) to demonstrate substantial equivalence to a predicate device. This is a common approach for imaging components like flat panel detectors where the primary concern is the technical performance of the imaging hardware itself, rather than diagnostic accuracy involving human interpretation when coupled with a full DR system.

Therefore, many of the requested points regarding clinical study design, expert involvement, and human reader performance are not applicable to this submission.

Here's a breakdown based on the provided text, addressing the points where information is available:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" in a separate table with pass/fail thresholds. Instead, it presents a comparison of key technical specifications between the proposed device (Venu1748V) and the predicate device (VIVIX-S 1751S). The implication is that meeting or exceeding the performance of the predicate device for these parameters constitutes "acceptance."

2. Sample Size Used for the Test Set and Data Provenance:

• Sample Size: Not applicable. The "test set" here refers to the non-clinical bench testing of the detector's physical performance characteristics. These tests are typically performed on a limited number of manufactured units (e.g., a few samples per batch) to ensure they meet specifications. The document does not specify the exact number of units tested for each parameter.
• Data Provenance: The company is iRay Technology Taicang Ltd., located in Taicang, Jiangsu, CHINA. The testing was performed internally or by a contracted lab. The data is retrospective in the sense that it was collected as part of the device's development and verification, prior to submission.

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

Not applicable. For non-clinical tests of a flat panel detector, "ground truth" is established by physical measurement standards and calibrated equipment, not by human expert consensus or clinical diagnosis. For example, MTF is measured using a phantom and analytical methods, not by radiologists.

4. Adjudication Method for the Test Set:

Not applicable. Since no human experts are establishing ground truth for diagnostic decisions, there's no need for adjudication.

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:

No. The document explicitly states: "Clinical data is not needed to characterize performance and establish substantial equivalence. The non-clinical test data characterizes all performance aspects of the device based on well-established scientific and engineering principles." This device is a hardware component (a flat panel detector), not an AI algorithm assisting human readers.

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

No. This is a hardware component. There is no "algorithm only" performance in the sense of an AI diagnostic tool. The detector captures raw image data.

7. The Type of Ground Truth Used:

The "ground truth" for the non-clinical tests consists of:

• Physical standards/measurements: For parameters like dimensions, pixel pitch, greyscales, power consumption, temperature/humidity ranges.
• Engineering metrics: For performance characteristics like MTF, DQE, spatial resolution, signal-to-noise ratio, uniformity, defect, minimum triggering dose rate, and low contrast resolution. These are established through standardized testing procedures using phantoms and calibrated instruments.
• Compliance with standards: Electrical safety (IEC/ES 60601-1, IEC60601-2-54) and EMC testing (IEC 60601-1-2) ensure the device meets predefined safety and electromagnetic compatibility benchmarks.
• Software verification: The software "iDetector" hazards, requirements specification, and design specification were tested against the intended design specification.

8. The Sample Size for the Training Set:

Not applicable. This is a hardware device. "Training set" typically refers to data used to train AI/machine learning models.

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

Not applicable, as there is no training set mentioned for an AI/ML model for this hardware device.

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