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Syngo Carbon Enterprise Access is indicated for display and rendering of medical data within healthcare institutions.

Syngo Carbon Enterprise Access is a software only medical device which is intended to be installed on recommended common IT Hardware. The hardware is not seen as part of the medical device. Syngo Carbon Enterprise Access is intended to be used in clinical image and result distribution for diagnostic purposes by trained medical professionals and provides standardized generic interfaces to connect to medical devices without controlling or altering their functions.

Syngo Carbon Enterprise Access provides an enterprise-wide web application for viewing DICOM, non-DICOM, multimedia data and clinical documents to facilitate image and result distribution.

The provided text is a 510(k) summary for the Syngo Carbon Enterprise Access (VA40A) device. It describes the device, its intended use, and compares it to a predicate device (Syngo Carbon Space VA30A). However, it explicitly states:

"No clinical studies were carried out for the product, all performance testing was conducted in a non-clinical fashion as part of verification and validation activities of the medical device."

Therefore, I cannot provide the information requested in your prompt regarding acceptance criteria, sample sizes, expert involvement, adjudication, MRMC studies, standalone performance, and ground truth establishment for a clinical study.

The document outlines "Non-clinical Performance Testing" and "Software Verification and Validation." These sections describe how the device's functionality was tested and validated to ensure it meets specifications and is substantially equivalent to the predicate device.

Here's what can be extracted about the "study" that proves the device meets "acceptance criteria" from a non-clinical performance testing and software verification/validation perspective:

1. Table of acceptance criteria and reported device performance:

The document broadly states that "The testing results support that all the software specifications have met the acceptance criteria" and "Results of all conducted testing were found acceptable in supporting the claim of substantial equivalence." However, it does not provide a specific, detailed table of acceptance criteria and quantitative performance metrics for each criterion. It rather focuses on demonstrating equivalence through feature comparison and general assertions of testing success.

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

The document mentions "non-clinical tests" and "software verification and validation testing." It does not specify the sample size for the test set or the provenance of the data used for this non-clinical testing (e.g., country of origin, retrospective or prospective). This testing would typically involve various types of simulated data, pre-recorded medical images, and functional tests rather than patient studies.

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

Since no clinical studies were performed and the testing was non-clinical and technical, there's no mention of "experts" establishing ground truth in the way it would be done for a clinical performance study. The "ground truth" for non-clinical software testing would be derived from the product's functional specifications and expected outputs.

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

Not applicable, as no clinical study with human interpretation/adjudication was conducted.

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, as no clinical studies, especially MRMC studies, were conducted. The device is a medical image management and processing system, not an AI-assisted diagnostic tool.

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

The testing was on the device's functionality as a standalone software, but this refers to its technical performance in image management and display, not a diagnostic algorithm.

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

For this type of device and testing, the "ground truth" would be the functional specifications and expected behavior of the software based on its design documents. For example, if a function is to display a DICOM image, the ground truth is that the image should be displayed correctly according to DICOM standards. It's not based on clinical or pathological findings.

8. The sample size for the training set:

Not applicable. This device is a medical image management and processing system, not an AI/ML diagnostic algorithm that requires a "training set."

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

Not applicable, as there is no training set for this type of device.

In summary, the provided document focuses on demonstrating substantial equivalence through a comparison of features with a predicate device and general non-clinical verification and validation activities, rather than a clinical performance study with defined acceptance criteria and statistical analysis of human or AI diagnostic performance.

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