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The OverStitch Endoscopic Suturing System is intended for endoscopic placement of suture(s) and approximation of soft tissue.

The OverStitch™ Endoscopic Suturing System and accessories are intended for endoscopic placement of sutures and approximation of soft tissue within the gastrointestinal tract utilizing either a dual channel or single-channel endoscope. The system is comprised of the Needle Driver Assembly and Anchor Exchange Device (collectively referred to as ESS), and accessories such as the Tissue Helix, Suture Cinch and Suture-Anchor Assembly devices. All devices are sterile packaged and designed for single use and are manufactured from various thermoplastic, silicone, stainless steel and other medical grade materials.

The provided text is a 510(k) summary for the OverStitch™ Endoscopic Suturing System and Accessories. It outlines the device's technical specifications and the non-clinical performance data used to demonstrate substantial equivalence to a predicate device. However, it explicitly states that clinical performance data was not required for this submission.

Therefore, I cannot provide information on acceptance criteria and a study proving the device meets those criteria from this document, as the document focuses on non-clinical testing for substantial equivalence, not on establishing device performance against clinical acceptance criteria through a clinical study.

Here's a breakdown of what can be and cannot be extracted from the provided text based on your request:

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

• Cannot be provided. The document details non-clinical performance testing (e.g., needle passing reliability, pull-off strength) to show equivalence to a predicate device, but it does not specify explicit "acceptance criteria" for these performance metrics or report quantitative results against such criteria in a manner suitable for a table. It only states that the testing "confirmed conformance to product specifications" and "equivalency between the subject and predicate devices."

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

• Cannot be provided. The document mentions "product testing" and "bench testing" but does not detail the sample sizes used for these tests. Data provenance (country of origin, retrospective/prospective) is also not mentioned for the non-clinical tests.

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. This document describes non-clinical bench testing and verification/validation activities for a physical medical device. The concept of "ground truth" established by experts, as would be relevant for diagnostic AI/imaging devices, does not apply to the type of testing described here.

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

• Not applicable. As above, the testing described is non-clinical performance testing of a physical device, not an interpretation-based study where adjudication would be relevant.

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. The document explicitly states: "Clinical Performance Data: Clinical testing was not required to demonstrate substantial equivalence." An MRMC study is a type of clinical study, typically for diagnostic devices or AI applications, which was not performed or submitted here.

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

• No. This device is a mechanical endoscopic suturing system; it is not an algorithm or AI system.

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

• Not applicable. "Ground truth" in the context of expert consensus, pathology, or outcomes data is relevant for clinical studies, particularly for diagnostic devices or AI. The testing described here is non-clinical performance testing against product specifications.

8. The sample size for the training set

• Not applicable. This device is a physical medical instrument, not a machine learning model, so there is no "training set."

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

• Not applicable. As above, this is not a machine learning model.

In summary, the provided FDA 510(k) summary focuses on demonstrating substantial equivalence for a physical medical device through non-clinical performance, materials, and sterilization testing, rather than presenting clinical study data with acceptance criteria for device performance.

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