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Xmaru1717 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.

Xmaru1717 is the TFT-based Flat Panel X-Ray Detector that keeps this Digitally based World going forward by providing the most important solution converting transmitted X-Ray into Digital Information. Xmaru1717 is a medical image processing unit. Especially, advanced digital imaging process allows considerably efficient diagnosis, all kind of information management, real-time sharing of image information on network. Xmaru1717 is an X-Ray image acquisition device that is based on flat-panel. This device should be integrated with an operating PC and a X-Ray generator. It can do to utilize as digitalizing x-ray images and transfer for radiography diagnostic

The provided text does not contain detailed information about specific acceptance criteria related to device performance (beyond general safety and electromagnetic compatibility) or a study proving it meets those criteria, especially in the context of AI/human-in-the-loop performance. Instead, it describes a medical device, the Xmaru1717 Digital Flat Panel X-Ray Detector, and its 510(k) submission process. The submission focuses on demonstrating substantial equivalence to predicate devices based on technological characteristics and general safety and performance standards.

Here's an analysis based on the information available in the text, addressing the requested points where possible, and noting where information is not provided:

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

The document does not provide a table of specific quantitative acceptance criteria for device performance (e.g., spatial resolution, DQE, MTF values) or associated reported performance values. The closest statement regarding performance is:

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

Not applicable. The submission is for a digital X-ray detector, which is a hardware component. There is no mention of a "test set" of patient data in the context of evaluating diagnostic accuracy or AI performance. The performance tests mentioned (EN/IEC 60601-1, EN/IEC 60601-1-2) refer to engineering and safety standards, not clinical data sets.

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

Not applicable. As there's no mention of a clinical "test set" for diagnostic performance or AI evaluation, there's no information about experts establishing ground truth.

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

Not applicable. See point 3.

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 presents a 510(k) submission for a digital flat panel X-ray detector. This product is a hardware component for capturing X-ray images, not an AI-powered diagnostic system. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to this submission and is not mentioned.

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

No. This device is a hardware component (a digital X-ray detector). It does not contain an "algorithm" in the sense of an AI-based diagnostic tool that would perform standalone interpretations. Its function is to convert X-rays into digital information.

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

Not applicable. Given the device is an X-ray detector, the "ground truth" for its performance would typically relate to engineering specifications and image quality metrics, not clinical outcomes or pathology.

8. The sample size for the training set

Not applicable. This device is a hardware component and does not involve AI algorithms that require a "training set."

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

Not applicable. See point 8.

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