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The values are used in conventional gastrointestinal endoscope to control the functions of water/gas injection and suction during the endoscopic operation.

Disposable Endoscope Valves Kit consists of Water/gas button, Attraction button, Biopsy cap and Water injection adapter. Disposable Endoscope Valves Kit connect the endoscope operating handle matched with the model, and install the water gas button into the water gas port of the operating handle; Align the positioning boss of the suction button with the card slot of the operating handle and install it into the suction port; Install the biopsy cap into the clamp opening of the operating handle.

The provided text describes the acceptance criteria and a study demonstrating the substantial equivalence of the "Disposable Endoscope Valves Kit" to a predicate device. However, it does not include information typically found in studies proving an AI/ML device meets acceptance criteria, such as specific performance metrics like sensitivity, specificity, or AUC, detailed sample sizes for test sets, expert qualifications, or adjudication methods for ground truth.

This document is a 510(k) summary for a medical device that does not appear to be an AI/ML device. It seems to be a physical medical accessory. Therefore, the questions related to AI/ML specific criteria (such as effect size of human readers with AI vs without AI, standalone algorithm performance, training set details) are not applicable to this submission.

The study described is a non-clinical performance data study, not a clinical study involving AI/ML.

Here's an attempt to answer the relevant questions based on the provided text, while noting the limitations due to the nature of the device:

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

The document frames "acceptance criteria" as meeting regulatory standards and demonstrating substantial equivalence to a predicate device through non-clinical testing. It doesn't list quantitative performance metrics for the proposed device in the way one would for an AI/ML diagnostic tool (e.g., sensitivity, specificity).

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

The document mentions "bench tests" were performed. For these non-clinical performance tests (Appearance, Physical properties), the specific sample size is not explicitly stated, nor is the data provenance. These would typically involve testing a certain number of manufactured units.

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

This question is not applicable as the device is a physical accessory and the evaluation is based on engineering and material standards, not diagnostic interpretation requiring expert ground truth in the AI/ML context.

4. Adjudication method for the test set

This question is not applicable for the same reasons as above. The evaluation of appearance and physical properties would follow established testing protocols and standards, not a clinical adjudication process.

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 such study was conducted or reported. This type of study is relevant for AI/ML diagnostic devices, which this device is not. The document explicitly states: "No Clinical Study is included in this submission."

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

This question is not applicable. The device is a physical endoscope accessory, not an algorithm.

7. The type of ground truth used

For the non-clinical tests (biological evaluation, sterilization, appearance, physical properties), the "ground truth" would be compliance with established international standards (ISO series) and predefined specifications for the physical characteristics of the device. This is not "expert consensus, pathology, or outcomes data" in the typical sense of AI/ML diagnostic validation.

8. The sample size for the training set

This question is not applicable as this is not an AI/ML device and therefore does not have a "training set."

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

This question is not applicable for the same reason as above.

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