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The VNUS Closure System is intended for endovascular coagulation of blood vessels in patients with superficial vein reflux.

VNUS® Closure® System

This 510(k) summary (K030557) is for the VNUS Closure System, an electrosurgical device for treating superficial vein reflux. The summary indicates that the device met acceptance criteria based on in-vitro testing and biocompatibility. However, it does not contain the detailed information necessary to fully answer all of the questions about a typical AI/ML device study. The provided document is from 2003, and the standards for reporting AI/ML device studies were not yet established.

Here's a breakdown of the available information based on the prompt's requirements:

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

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

This information is not provided in the document. The testing was "in-vitro," meaning in a laboratory setting, not on human subjects or clinical data in the way an AI/ML device would be tested.

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)

This information is not applicable/provided. The testing was in-vitro, and there's no mention of expert-established ground truth in the context of clinical images or data analysis.

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

This information is not applicable/provided. As the testing was in-vitro, there's no indication of an adjudication process for a test set in a clinical context.

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

This information is not applicable/provided. This device is not an AI/ML diagnostic or assistive device that would involve human readers.

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

This information is not applicable/provided. This device is not an algorithm, but a physical medical device.

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

The "ground truth" for the device's performance appears to be established by the functional results of the in-vitro testing for safety and effectiveness, and biocompatibility testing for material safety. This isn't a "ground truth" in the sense of clinical annotations for a diagnostic algorithm.

8. The sample size for the training set

This information is not applicable/provided. The device is not an AI/ML algorithm that requires a training set.

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

This information is not applicable/provided. The device is not an AI/ML algorithm.

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