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The Vision Sciences (VSI) Bronchoscope and Disposable Sheath System will be used for diagnostic examination of the inside of the trachea or windpipe and other major air passages of the lungs. It may also be used to gather specimens and to find and remove foreign objects in the lungs.

The VSI Model B-F100 Bronchoscope with BS-F21 Disposable EndoSheath consists of a reusable bronchoscope with a D-shaped cross section containing fiberoptic illumination and imaging bundles and a sterile, disposable, protective sheath containing the suction/biopsy channel. The sheath is removed and disposed of following each procedure.

The provided text describes Vision Sciences Inc.'s 510(k) Premarket Notification for their Flexible Fiberoptic Bronchoscope with Disposable EndoSheath. However, it does not include detailed acceptance criteria or a study that rigorously proves the device meets those criteria in the way typically required for a comprehensive AI/ML device submission as outlined in your request.

The submission is for a physical medical device (a bronchoscope and sheath) and focuses on functional, biocompatibility, and microbial barrier testing, rather than an AI/ML algorithm's performance. Therefore, I cannot extract the specific information you've requested about AI/ML device performance, such as sensitivity, specificity, MRMC studies, effect sizes of AI assistance, or details about training/test set ground truth establishment for an AI algorithm.

Based on the provided text, here is what can be inferred relating to acceptance criteria and performance, but it focuses on the device's physical and functional aspects, not AI/ML performance:

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

The following points cannot be answered based on the provided text, as they pertain to AI/ML device evaluation, which is not described in this 510(k) summary for a physical bronchoscope:

2. Sample size used for the test set and the data provenance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts.
4. Adjudication method for the test set.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and if so, what was the effect size of how much human readers improve with AI vs without AI assistance.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.).
8. The sample size for the training set.
9. How the ground truth for the training set was established.

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The EndoSheath provides a sterile, disposable protective covering for the scope to be used during endoscopic examination of the sigmoid colon.

The EndoSheath for use with the VSI Model S-F100 and S-V100 Sigmoidoscopes consists of a sterile, disposable, protective sheath which covers the pation of the scope during a clinical procedure. The sheath is removed and disposed of following each procedure.

The provided text describes a 510(k) Premarket Notification for a medical device, the Modified EndoSheath® for VSI Flexible Sigmoidoscopes, by Vision Sciences Inc. This document focuses on demonstrating the substantial equivalence of the modified device to previously cleared predicate devices through functional and microbial barrier testing.

However, the provided text does not contain the level of detail required to fulfill all the requested information categories, particularly regarding the specifics of clinical studies, expert involvement, and ground truth establishment typically found in comprehensive device performance reports for AI/diagnostic tools. The device described is a physical sheath, and the testing focuses on its physical properties and barrier function, not diagnostic accuracy in the way an AI algorithm would be evaluated.

Therefore, for several categories, the response will state that the information is "Not applicable" or "Not provided in the text."

Here's the breakdown based on the provided text:

1. Table of Acceptance Criteria and 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)

• Sample Size for Test Set: Not specified in the provided text. The tests mentioned are in vitro functional tests and microbial barrier testing, not tests on a clinical patient sample or data set.
• Data Provenance: Not applicable, as these were in vitro laboratory tests, not human data.

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. The ground truth for these in vitro physical and microbial barrier tests would be established by laboratory testing protocols and established scientific methods, not by expert human interpretation of medical data.

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

• Not applicable. Adjudication methods like "2+1" or "3+1" are typically used for establishing ground truth in clinical or image-based studies where human interpretation is involved. This document describes in vitro functional and barrier tests.

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. An MRMC comparative effectiveness study was not done. This device is a physical sterile sheath, not an AI or diagnostic tool designed to assist human readers.

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

• Not applicable. This device is a physical product, not an algorithm.

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

• The "ground truth" for the tests performed (tensile/elongation, image quality, microbial barrier) would be established by physical standards and microbiological testing protocols. For example, the ground truth for microbial barrier testing would be the absence of bacteriophage penetration under specified conditions. This is not clinical ground truth like pathology or outcomes data.

8. The sample size for the training set

• Not applicable. This is not an AI/machine learning device, so there is no concept of a "training set."

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

• Not applicable, as there is no training set for this type of device.

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