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This equipment has been designed to be used to process signals from an endoscope and convert it to a signal that can be displayed on the monitor.

The Medical Video Endoscope Image Processor PV210 is a video processor which is intended and designed for use during endoscopic procedures. It receives and processes image signals from digital endoscope and outputs signals to a medical monitor to display. PV210 has integrated software to control the operation of the device. PV210 is a reusable device and do not require sterilization before use, as there is no direct/in-direct patient contact material. It needs to be cleaned and disinfected before use, the cleaning and disinfection method is validated and recommend in the User Manual. The equipment is for use in a hospital or qualified medical institution. It is only to be used by skilled physicians trained in clinical endoscopic techniques and procedures.

This document is a 510(k) summary for the Pusen™ Eview™ Medical Video Endoscope Image Processor, Model PV210. Primarily, this device processes signals from an endoscope and converts them for display on a monitor.

Here's an analysis of the provided text for acceptance criteria and study information:

Acceptance Criteria and Device Performance

The document does not explicitly state numerical acceptance criteria in a dedicated table format. Instead, it indicates compliance with recognized safety and performance standards. The "device performance" reported is that the device "conforms to the requirements" of these standards and "met all requirements" of performance specifications.

Study Information:

1. Sample size used for the test set and the data provenance:
   The document mentions "bench testing" and "software verification and validation testing," which implies that hardware and software components of the device were tested. However, it does not specify any sample sizes for these tests. Data provenance (e.g., country of origin, retrospective/prospective) is also not specified for the testing conducted.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   This information is not provided. The testing described appears to be engineering and software validation rather than clinical performance evaluation that would typically involve expert-established ground truth.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
   Since the testing described is technical compliance and performance verification, adjudication methods typically associated with clinical studies (like 2+1 or 3+1 for image interpretation) are not applicable and not mentioned.

4. 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:
   An MRMC comparative effectiveness study was not done and not mentioned. This device is an image processor, not an AI-powered diagnostic tool, and the submission primarily focuses on substantial equivalence to a predicate device based on technical specifications and safety. There is no mention of AI assistance or human reader improvement.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
   The document describes "Software verification and validation testing" and "Bench testing of performance specifications." These would be considered standalone evaluations of the device's technical functionality, but not in the context of an "algorithm only" performance for diagnostic accuracy as implied by the question. The device processes signals; it doesn't perform diagnostic algorithms.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   For the technical and safety testing conducted, the "ground truth" would be the requirements of the specified electrical, EMC, and software standards, and the pre-defined performance specifications. There is no mention of clinical ground truth like pathology or expert consensus.

7. The sample size for the training set:
   This information is not applicable and not provided. The device is an image processor, not a machine learning or AI-driven system that would require a "training set."

8. How the ground truth for the training set was established:
   This information is not applicable and not provided for the reasons stated above.

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