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The MOVIEW is a software device vicently , print and save cardia. In the same income studio refrieve refrieve, display, profit and into in a mage studio cathelerization faboration and to view these real-Time mode PC images on Cu

The MDVIEW is a software device intended to retrieve, display, print and save cardiac catheterization laboratory image studies from CD-R media, and to view these images on a PC computer in real time mode. Digital imaging from the cardiac catheterization laboratory is recorded on the CD-R in conformance with the DICOM standard. These loss free images provide physicians with a valuable tool for diagnostic review and analysis.

The provided document is a 510(k) premarket notification for a medical image communication and storage device (MDVIEW-MEDCON's DICOM Viewer). It does not contain typical acceptance criteria or a detailed study proving device performance in the way modern AI/ML medical devices would.

This document is from 1997, a time before the widespread use of AI/ML in medical devices, and it concerns a Picture Archiving and Communication System (PACS) device. For such devices, "performance" relates to their ability to accurately display, store, and retrieve medical images, ensuring image integrity and compatibility with standards like DICOM. The review process for such devices would focus on demonstrating technical functionality and equivalence to existing devices rather than a detailed clinical performance study with statistical endpoints.

Therefore, many of the requested sections about acceptance criteria, study details, human reader performance, and ground truth establishment, as they apply to clinical performance of an AI/ML algorithm, are not applicable or available in this document.

However, I can extract the relevant information that is present:

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

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

• Not Applicable / Not Provided. This document does not describe a clinical performance study with a test set of patient data to evaluate diagnostic accuracy. The assessment focuses on technical and functional equivalence to a predicate device.

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

• Not Applicable / Not Provided. No ground truth establishment for a test set is mentioned, as there was no clinical performance study in the context of an AI/ML diagnostic aid.

4. Adjudication method for the test set

• Not Applicable / Not Provided. No test set or adjudication method 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

• No. This device is a PACS viewer, not an AI diagnostic aid. Therefore, no MRMC study or assessment of human reader improvement with AI assistance was performed or is relevant to this submission.

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

• Not Applicable / Not Provided. This is a PACS viewer, not an algorithm. Standalone performance as understood for AI/ML algorithms is not relevant to this device's regulatory review.

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

• Not Applicable / Not Provided. The concept of "ground truth" for a diagnostic outcome is not relevant to a PACS device's functional equivalence review. The "truth" in this context refers to the accurate display and storage of the original, unadulterated medical images.

8. The sample size for the training set

• Not Applicable / Not Provided. There is no mention of a training set as this is not an AI/ML device.

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

• Not Applicable / Not Provided. No training set or ground truth establishment is described.

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