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SOFSILK* suture is intended for the approximation of tissues where prolonged retention of tensile strength is required.

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

This document does not describe a medical device that uses AI/ML with a testing framework and outcomes. Instead, it is a 510(k) premarket notification for a *surgical suture (SOFSILK)**.

Therefore, most of the requested information (acceptance criteria for AI performance, sample sizes for test/training sets, expert qualifications, MRMC studies, standalone performance, etc.) is not applicable to this type of device.

Here's how I can address the parts of your request that are relevant, based on the provided text:

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

The provided text does not include specific quantitative acceptance criteria or performance metrics typical for AI/ML devices (e.g., accuracy, sensitivity, specificity). For a surgical suture, performance is typically assessed through biocompatibility, tensile strength, knot security, and sterility, often against recognized standards like USP (United States Pharmacopeia) or ISO. This document only states:

2. Sample sized 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 it's a physical device, not a data-driven AI/ML system. There are no "test sets" of data in the context of AI. Testing for sutures would involve material science evaluations, animal studies (historically), and clinical experience, none of which are detailed here.

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 AI sense does not exist for a surgical suture. The performance of the suture is determined by physical and biological properties.

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

This is not applicable.

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 for evaluating interpretative tasks, not for physical medical devices like sutures.

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

This is not applicable. This device is a physical product and does not involve an algorithm.

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

This is not applicable. The "truth" for a suture is its physical and biological properties and its ability to perform its intended function in vivo, which is established through various testing methods and adherence to material standards (like ISO 10993-1 for biocompatibility).

8. The sample size for the training set

This is not applicable. There is no "training set" for a physical suture device.

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

This is not applicable.

In summary: The provided document is for a traditional surgical suture. The questions posed in the prompt are designed for AI/ML-driven medical devices, and as such, most are not relevant to this product. The key information provided relates to the device's identity, intended use, and material compliance with an ISO standard for biocompatibility.

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