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Modified Sofsilk* Suture has indications for use in general soft tissue approximation and/or ligation, including cardiovascular, ophthalmic, microsurgery and neural tissue.

Modified Sofsilk * Suture is a nonabsorbable, sterile, flexible multifilament thread, prepared from silk fiber produced from the species Bombyx mori. The primary component of silk is the high molecular weight fibrous polymer fibroin.

The provided text is a 510(k) summary for the Modified Sofsilk Suture and does not describe a study involving an AI/CAD/software device, therefore most of the requested information is not applicable. The document focuses on establishing substantial equivalence of a physical medical device (suture) to a predicate device.

Here's an analysis based on the information available:

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

The document does not explicitly state "acceptance criteria" in the way it would for a performance study of a device like AI. Instead, it refers to the device being "substantially equivalent" to a predicate device, Sofsilk* (K964581). This implies that the Modified Sofsilk Suture met the same performance characteristics as the predicate device, which would include physical properties relevant to sutures (e.g., tensile strength, knot security, biocompatibility).

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

This information is not applicable as the document describes a physical medical device (suture) and not a software/AI device that would involve a test set of data. The "test set" in this context would refer to the samples of the Modified Sofsilk Suture manufactured for testing physical properties to demonstrate equivalence. The document does not specify the number of suture samples tested or the provenance of any data beyond the general manufacturing and testing processes that would occur for such a device.

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. Ground truth, in the context of AI/software devices, refers to expert labeling of data. For a physical device like a suture, "ground truth" would relate to objective measurements of its physical and biological properties against established standards, not expert interpretations of data.

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

This is not applicable. Adjudication methods are typically used in studies involving human interpretation of data, often in the context of AI performance evaluation, to resolve discrepancies in expert opinions.

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. MRMC studies are used to evaluate the performance of human readers, with or without AI assistance, on a collection of cases. This document concerns a physical surgical suture.

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

This is not applicable. This question pertains to the performance of an algorithm without human intervention, which is not relevant to a physical medical device.

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

As explained in point 3, the concept of "ground truth" as typically defined for AI/software devices is not applicable. For this physical medical device, demonstrating compliance with "ground truth" would involve proving that the suture meets established physical, chemical, and biological standards through laboratory testing (e.g., tensile strength, biocompatibility, sterility).

8. The sample size for the training set

This is not applicable. There is no "training set" for a physical medical device like a suture as there is no algorithm being trained.

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

This is not applicable for the reasons stated above; there is no training set for this type of device.

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