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The JPACS radiology suite provides access to clinical reports and images via computer networks. Given the following information objects, the JPACS radiology suite may be used on any part of the human anatomy: CR, CT, MR, XA, SC, US, NM, PET, and RT. The suite also provides short, medium, and long term storage of these clinical images from any authorized workstation within an intranet or over the Internet. The full feature imaging suite can be used for in-house image distribution, on-call teleradiology, and advance visual display features. The JPACS radiology suite also includes functionality for generation and distribution of clinical reports.

The JPACS radiology suite is intended to provide access to clinical reports and images via computer networks. It is also intended to provide short, medium and long range storage of clinical images from any authorized workstation within an intranet or over the Internet. The full feature imaging suite can be used for in-house image distribution, on-call teleradiology, and advance visual display features. The JPACS radiology suite also includes functionality for generation and distribution of clinical reports. Picker adheres to FDA 21 CFR 820 and voluntary standards for safety and effectiveness (UL 187) all of which mandate that components are tested to minimize hazards (electrical, mechanical, and radiation). In addition, the system is designed to conform to IEC 601-1. The subject device has the same technological characteristics as a legally marketed predicate device. Specifically, the features, specifications, materials, and mode of operation are equivalent. External communication uses DICOM V. 3.0 NEMA Standards Publication Parts PS3.1 through PS3.13, dated 1996.

This document describes the JPACS (Picture Archiving & Communication System) and its substantial equivalence to a predicate device, the Medisurf manufactured by Algotec Systems Ltd. The information provided focuses on the regulatory submission and does not include a specific study proving the device meets acceptance criteria distinct from the substantial equivalence claim.

Therefore, many of the requested details about a specific performance study are not available in the provided text. The submission primarily asserts equivalence based on shared technological characteristics and intended use.

Here's the breakdown of the available information:

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

The document does not explicitly state quantitative acceptance criteria or report device performance in terms of metrics like sensitivity, specificity, accuracy, or other benchmarked performance indicators. Instead, the "acceptance criteria" here refers to the FDA's regulatory framework for substantial equivalence.

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

Not applicable. This was a 510(k) premarket notification for substantial equivalence, not a clinical performance study with a test set of data. The submission relies on demonstrating equivalence to a predicate device rather than a new performance evaluation on a specific dataset.

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

Not applicable, as no such test set or ground truth establishment process is described.

4. Adjudication method for the test set

Not applicable, as no such test set or adjudication process is described.

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. The JPACS is a Picture Archiving and Communication System (PACS), which is infrastructure for managing and displaying medical images and reports. It is not an AI-powered diagnostic tool, and therefore, an MRMC study related to AI assistance for human readers is not relevant to this device's type or its 510(k) submission.

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

Not applicable. The JPACS is a system facilitating human interaction with images and reports, not an algorithm performing a diagnostic task independently.

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

Not applicable, as no internal performance study using ground truth is described. The "ground truth" for the 510(k) submission is the demonstrated safety and effectiveness of the predicate device.

8. The sample size for the training set

Not applicable. No algorithm training is mentioned or relevant for this type of device and submission.

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

Not applicable. No algorithm training is mentioned or relevant.

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