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The WV3000T Digital X-ray Direct Imaging Flat Panel Detector System provides a digital image capture capability for conventional radiographic examinations (excluding fluoroscopic, angiographic, and mammographic applications). The device has application wherever conventional screen-film systems are currently used.

The WV300T Digital X-ray Direct Imaging Flat Panel Detector System is used to directly capture and convert conventional projection X-ray images to digital images. A image preview function can be displayed on a review monitor for viewing. The diagnostic image can be transmitted through LAN for diagnostic viewing and printing. The device provides digital image capture for conventional radiographic examinations, excluding fluoroscopic, angiographic and mammography applications. The system differs from traditional X-ray systems in that instead of exposing a film for subsequent wet chemical processing to create a hardcopy image, a device called a detector array is used to capture the image in electronic form. The digital data are then used to produce hardcopy and softcopy images. The WV3000T Digital X-ray Direct Imaging Flat Panel Detector System is composed of the following: A detector array is used to capture the diagnostic image, and transfer the image to system controller in digital format. An multi-box is used to control detector array, harmonize the working between the array controller and high-voltage generator for exposal synchronization. A system controller is used to enter patient demographic information, initiate the exposure process, review captured images, and accept or reject captured images. The system controller is also used to send images to a hardcopy printer, workstation, or archive, and manage images temporarily stored in its database. Here, the system controller is the software device and which should install in the PC hardware system purchased by themselves of customer. The system controller also can make some disposal for the original image, such as gain, offset and defective pixel correction. By capturing, previewing, and storing and image, the system enables an operator to check the quality of an image at the time of exposure without having to develop a film.

The provided text is a 510(k) summary for the "WV3000T Digital X-ray Direct Imaging Flat Panel Detector System". This document focuses on establishing substantial equivalence to a predicate device and contains limited information regarding specific acceptance criteria and detailed study results typical for an AI/ML device submission.

Based on the provided text, the device is not an AI/ML device but a digital X-ray detector system. Therefore, many of the requested points, especially those related to AI/ML specific performance metrics, ground truth, training sets, and human-in-the-loop studies, are not applicable or not addressed in this type of submission.

Here's an attempt to answer the questions based only on the provided text:

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

The document does not explicitly state "acceptance criteria" in the way it would for an AI/ML diagnostic algorithm (e.g., target sensitivity, specificity, or AUC thresholds). Instead, the performance assessment is based on substantial equivalence to a predicate device.

It's important to note that direct numerical performance metrics (like DQE, MTF, SNR values) are not provided in this summary, though they would typically be part of the technical documentation for such a device. The "All the information about the device performance has provided" and "The Clinical Test Report has provided" statements imply these tests were done, but the details are not summarized here.

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 mentions "The Clinical Test Report has provided" but does not specify any sample size for a test set. There's also no information regarding data provenance (country of origin, retrospective/prospective). This is typical for a 510(k) for an imaging hardware device, where clinical performance is often demonstrated through comparison to a predicate and technical specifications rather than extensive clinical studies with specific test sets and ground truth for diagnostic accuracy.

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/Not provided. The device is a digital X-ray detector, not a diagnostic algorithm that interprets images. Ground truth for diagnostic interpretations would not be directly established for the performance of the detector system itself in this context.

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

Not applicable/Not provided. As the device is hardware for image capture, not an AI/ML diagnostic tool, an adjudication method for a test set of diagnoses is not relevant to this submission.

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. This is not an AI/ML device, so an MRMC study comparing human readers with and without AI assistance is not relevant and was not performed.

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

No. This is a hardware device (X-ray detector), not an algorithm.

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

Not applicable/Not provided. The device's performance relates to image capture quality and safety, not diagnostic interpretation, so "ground truth" for a diagnosis is not directly assessed for the device itself in this summary.

8. The sample size for the training set

Not applicable/Not provided. This is not an AI/ML device, so there is no "training set" in the machine learning sense.

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

Not applicable/Not provided. As there is no AI/ML training set, this question is not relevant.

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