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The DEFENDO EUS Air/Water Valve is intended to be used to control the air/water function of an endoscope during an GI Endoscopic procedure.

The DEFENDO EUS Suction Valve is intended to be used to control the suction function of an endoscope during a GI Endoscopic procedure.

The DEFENDO EUS Air/Water Valve and the DEFENDO EUS Suction Valve are accessories to an echoendoscope.

The EUS Air/Water valve allows the end user to control air or CO2 insufflation down the endoscope's accessory channel, control water used to wash the lens of the endoscope and insufflate a balloon at the distal end of the echoendoscope

The DEFENDO EUS Suction valve allows the user to control suction through the echoendoscope's accessory channel and suction to the balloon at the distal end of the echoendoscope.

Both devices are single-use devices, supplied sterile.

The provided document is a 510(k) summary for a medical device called "DEFENDO Single Use Valve Kit AW/S Valves - 2pc for OLYMPUS EUS". This document primarily focuses on demonstrating substantial equivalence to a predicate device, specifically for an extended shelf life. It is not a study proving the device meets acceptance criteria in the context of an Artificial Intelligence (AI) or machine learning (ML) enabled medical device, where terms like "ground truth," "expert consensus," "MRMC study," and "training set" are core to the evaluation.

Therefore, I cannot fulfill all parts of your request based on the provided text. The document describes a biological/mechanical device and its non-clinical performance testing for an extended shelf life, not the performance of an AI/ML algorithm.

However, I can extract the information related to the acceptance criteria and study results that are present for this specific type of device.

Here's what can be extracted and what cannot:

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

This information is present for the non-clinical performance testing of the mechanical device.

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

The document states that "Identical testing was conducted on both the predicate (cleared) 1 year version of the device and proposed 3 year shelf life version of the device." It then clarifies, "The three year shelf life summary is below." However, the specific sample size (N) for each test is not provided. The testing is physical/mechanical in nature, not data-driven in the sense of patient data. Therefore, "country of origin of the data" or "retrospective/prospective" are not applicable.

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 is not applicable as the document describes non-clinical performance testing of a physical medical device, not an AI/ML algorithm requiring expert ground truth for image interpretation or diagnosis.

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

This is not applicable for the same reason as point 3.

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 is not applicable as the document is not about an AI-assisted device.

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

This is not applicable as the document is not about an AI algorithm.

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

This is not applicable in the context of AI/ML. For this mechanical device, the "ground truth" for each test is a direct measurement against a predefined physical, mechanical, or functional standard (e.g., does it leak? does the spring compress within range?).

8. The sample size for the training set

This is not applicable as the document does not describe an AI/ML device with a training set.

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

This is not applicable as the document does not describe an AI/ML device with a training set or ground truth in that context.

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