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Ambra PACS software is intended for use as a primary diagnostic and analysis tool for diagnostic images for hospitals. imaging centers, radiologists, reading practices and any user who requires and is granted access to Patient image, demographic and report information.

Ambra Pro Viewer, a component of Ambra PACS, displays, modifics and manages diagnostic quality DICOM images including 3D visualization and reordering functionality.

Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary diagnosis or image interpretations. Mammographic images may only be viewed using cleared monitors intended for manmography Display.

Not intended for diagnostic use on mobile devices.

Ambra ProViewer, a component of Ambra PACS, displays, modifies, and manages diagnostic quality DICOM images, including 3D visualization and reordering functionality. It is designed to target standards-compliant, cross-platform web browsers with an underlying architecture built on top of ReactJS and Material-UI, as well as WebGL2 for advanced visualization tools. The Ambra ProViewer is designed to utilize modern web application APIs.

Ambra PACS is considered a 'Continuous Use' device. This device is compliant with HIPAA/HITECH, Safe Harbor, and 21 CFR Part 11 regulations regarding patient privacy (such as restricting access to particular studies, logging access to data), data integrity, patient safety and best software development and validation practices.

The provided document is a 510(k) summary for the Ambra PACS including Ambra ProViewer. It discusses the device's substantial equivalence to predicate devices but does not contain the detailed acceptance criteria and study results in the format requested, especially regarding specific performance metrics, sample sizes for test/training sets, expert qualifications, or MRMC studies.

Therefore, I cannot fully complete the requested table and answer all questions with the information given.

Here's what can be extracted and what is missing:

1. Table of acceptance criteria and the reported device performance

Missing Information: Specific quantitative acceptance criteria (e.g., minimum accuracy, sensitivity, specificity, or performance thresholds for visualization/measurement tools) are not provided. The document primarily focuses on demonstrating "substantial equivalence" rather than reporting specific performance metrics against defined criteria.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Missing Information: The document states "validation testing of DICOM images" was conducted using "the same reference DICOM images" as the predicate device, but it does not specify the sample size of the test set nor the data provenance (e.g., country of origin, retrospective or prospective nature).

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)

Missing Information: The document does not provide any information on the number of experts, their qualifications, or how ground truth was established for "validation testing of DICOM images."

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

Missing Information: The document does not specify any adjudication method for the test set.

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

Missing Information: The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it refer to AI assistance or its effect size on human reader performance. The device is described as a PACS viewer with advanced visualization, not an AI diagnostic aid.

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

Answer: Yes, a standalone evaluation was likely conducted in the form of "validation testing of DICOM images to demonstrate substantially equivalent performance of the measurement and visualization tools." The phrasing implies an assessment of the software's inherent functionality (algorithm only) against the predicate. However, detailed results are not provided.

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

Answer (Inferred): Given the context of PACS viewers and "measurement and visualization tools," the ground truth was likely established by reference DICOM images where measurements or visualization characteristics were already known or precisely quantifiable, serving as a "gold standard" for the software's performance, but this is not explicitly stated. It's unlikely to be pathology or outcomes data for a viewer device.

8. The sample size for the training set

Missing Information: The document does not mention a training set. This is a PACS viewer, not typically an AI model that requires a distinct "training set" in the context of deep learning. The "validation testing" refers to evaluating the software's functionality, not training an algorithm.

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

Missing Information: Since a training set isn't mentioned or applicable in the context of this document, the method for establishing its ground truth is also not applicable/missing.

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