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Zenis is intended for viewing of images acquired from Fluoroscopic X-ray system when installed on suitable commercial-standard PC hardware. Zenis is intended for use as a primary diagnostic and analysis workstation in Radiology or other departments. It is also intended for use as a clinical review workstation throughout the healthcare facility and may be part of a larger PACS configuration.

Zenis is a PC-based DICOM workstation platform which provides scalable image and data management solutions for medical imaging. This software-based product provides capabilities for the acceptance, transmission, printing, display, storage, editing and digital processing of medical images and associated data. Zenis may be combined with a PACS network or connected directly to a modality through the use of DICOM networking.

The provided text is a 510(k) summary for the Zenis Picture Archiving and Communications System (PACS). This document primarily focuses on establishing substantial equivalence to a predicate device (GE Medical Systems Information Technologies RA600) rather than providing detailed acceptance criteria and a study demonstrating the device meets those criteria in a typical AI/ML medical device context.

Based on the provided text, the following points can be extracted or reasonably inferred, but many of the requested details are not available as this document is not a performance study report for a novel AI algorithm.

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

The document does not explicitly state quantitative acceptance criteria for device performance as would be found in a study for a new diagnostic algorithm. Instead, it relies on the concept of substantial equivalence to an existing predicate device.

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

The document mentions "performance test and clinical evaluation" but does not provide any details on the sample size used, the type of data (e.g., specific image modalities or pathology types), or its provenance (country of origin, retrospective/prospective). This is typical for a PACS system which is a general image management and viewing device, not a diagnostic algorithm analyzed for specific performance metrics on a dataset of cases.

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

This information is not provided. As the device is a PACS system for image viewing and management, the concept of "ground truth" as it applies to diagnostic accuracy for an AI algorithm is not directly relevant here. The evaluation would likely focus on system functionality, image quality for viewing purposes, and adherence to DICOM standards, not diagnostic accuracy against a ground truth.

4. Adjudication method for the test set

This information is not provided. See the explanation for 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 MRMC comparative effectiveness study was done or reported. The Zenis device is a PACS system, not an AI-assisted diagnostic tool. Therefore, the concept of improving human readers with AI assistance does not apply in this context.

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

Not applicable. The Zenis device is a PACS system, not a standalone diagnostic algorithm.

7. The type of ground truth used

Not applicable in the context of diagnostic accuracy. For a PACS system, "ground truth" would relate to the accurate display, storage, and transmission of medical images as per DICOM standards and clinical requirements. The document implies that the "performance test and clinical evaluation" would have verified these aspects.

8. The sample size for the training set

Not applicable. As a PACS system, this device is not an AI/ML algorithm that undergoes a "training" phase with a specific dataset.

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

Not applicable. See explanation for point 8.

In summary:

The provided 510(k) summary for the Zenis PACS focuses on establishing substantial equivalence based on the device's functions and characteristics being "almost same" as the predicate device (RA600). It makes a general claim that "performance test and clinical evaluation indicates that the new device is as safe and effective as the predicate devices," but it does not provide any specific details about the methodology, data sets, expert involvement, or quantitative results that would typify a study validating a diagnostic AI device against explicit acceptance criteria and ground truth.

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