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The MicroSpan Hysteroscope Sheath is indicated for use in providing access to the uterine cavity for the Imagyn Hysteroscope or other hysteroscopic instruments during diagnostic and operative hysteroscopic procedures.

The MicroSpan Hysteroscope Sheath is offered with irrigation/insufflation and aspiration ports and an instrument channel. The device is provided sterile, for single-use, and is made from both metal and plastic components.

This document is a 510(k) summary for a medical device called the "MicroSpan Hysteroscope Sheath." It describes the device, its intended use, and comparisons to predicate devices. However, the information provided does not include details about acceptance criteria or a study that proves the device meets specific performance metrics in the way you've outlined for an AI/ML medical device.

The provided text describes a traditional hardware medical device (a hysteroscope sheath), not an AI/ML software device. Therefore, many of your requested points (like sample size for test/training sets, ground truth establishment, MRMC studies, standalone algorithm performance, number of experts for ground truth) are not applicable to this type of device submission.

Here's a breakdown of what can be extracted and what is missing based on the provided text, recognizing the difference in device type:

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

Missing: Specific quantitative acceptance criteria (e.g., "leak rate less than X mL/minute," "flow rate greater than Y mL/minute at Z pressure"). The description is very general.

Regarding the other points, they are largely not applicable or not provided in the context of this traditional medical device summary:

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

• Not Applicable/Not Provided: For a physical device like a hysteroscope sheath, "test set" in the context of data for AI/ML algorithms doesn't apply. The "tests" performed were likely bench tests on a specific number of manufactured units. The document does not specify the number of units 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)

• Not Applicable: "Ground truth" in the AI/ML sense (e.g., expert consensus on image interpretation) is not relevant for a hysteroscope sheath. Device performance would be assessed against engineering specifications.

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

• Not Applicable: This relates to expert review for AI/ML ground truth, which doesn't apply here.

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

• Not Applicable: This type of study is for evaluating observer performance with or without AI assistance, which is not relevant for a physical medical instrument like a hysteroscope sheath.

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

• Not Applicable: This refers to AI algorithm performance without human interaction, which is not applicable to this physical device.

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

• Not Applicable/Information Not Detailed: For a physical device, "ground truth" would be engineering specifications and functional requirements. The document mentions "non-clinical tests" and "leak and flow testing," implying performance was measured against predetermined criteria, but the specifics of these criteria are not provided.

8. The sample size for the training set

• Not Applicable: There is no "training set" in the AI/ML sense for this device.

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

• Not Applicable: There is no "training set" or "ground truth" establishment in the AI/ML sense for this device.

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