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Reinforced Absorbable Synthetic Surgical Mesh is indicated for use in the surgical repair of damaged or ruptured soft tissue where reinforcing or bridging materials are needed. This device is intended for one-time use.

The reinforced absorbable synthetic surgical mesh is a foam encased mesh. The foam component is a biocompatible elastomeric polymer that is bioabsorbable. This elastomer exhibits a high percent elongation, while possessing good tensile strength and good recovery characteristics. It has been found to be non-antigenic, non-pyrogenic, and elicits a slight, but typical, tissue reaction during absorption. Therapeutically, the foam component provides wound support as well as increases the opportunity for bridging to occur due to its porosity. The mesh component is a woven polydioxanone mesh that is used to provide wound support during the healing process. It has been found to be non-antigenic, non-pyrogenic, and also elicits a slight, but typical, tissue reaction during absorption. Therapeutically, the mesh component retains strength for a period of time long enough to permit sufficient healing, preventing wound dehiscence.

The provided text describes a 510(k) premarket notification for a medical device, the "Reinforced Absorbable Synthetic Surgical Mesh." This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than proving efficacy or meeting specific performance acceptance criteria through a standalone study. Therefore, much of the information requested in the prompt, such as detailed acceptance criteria, specific performance metrics, sample sizes for test and training sets, expert qualifications, and adjudication methods for AI performance, is not applicable or available in this document.

Here's an analysis based on the provided text, addressing the prompt's points where possible:

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

As this is a 510(k) submission, the "acceptance criteria" are primarily related to demonstrating substantial equivalence to predicate devices, not specific performance metrics against a pre-defined standard in the way an AI algorithm might be evaluated. The "reported device performance" is framed in terms of similarity to the predicate devices.

2. Sample size used for the test set and the data provenance

• Sample size for test set: Not applicable. No "test set" in the context of evaluating an AI algorithm or a new clinical performance study was conducted. The assessment relies on existing data from predicate devices.
• Data provenance: The data provenance is from pre-existing 510(k) submissions for the predicate devices: PDS™ Mesh (K844228) and AFB (Absorbable Foam Buttress) (K014183). This is essentially retrospective review of prior regulatory submissions for similar devices. The country of origin of the original predicate device data is not specified in this document.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. No "test set" requiring expert-established ground truth was used for this 510(k) submission. The FDA's review process itself involves regulatory experts.

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

Not applicable. No test set requiring expert adjudication was part of this submission. The "adjudication" in a 510(k) lies with the FDA's regulatory review.

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 device is a surgical mesh, not an AI-powered diagnostic tool. Therefore, an MRMC study related to human readers and AI assistance is entirely irrelevant to this submission.

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

Not applicable. This is a physical medical device (surgical mesh), not an algorithm.

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

The "ground truth" for this 510(k) submission is the regulatory acceptance and established performance of the predicate devices (PDSTM Mesh K844228 and AFB K014183) based on their own prior documentation regarding indications, materials, and functional characteristics. The submission argues that the new device meets the 'ground truth' of being substantially equivalent to these already-marketed devices.

8. The sample size for the training set

Not applicable. No "training set" in the context of AI development was used.

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

Not applicable. No "training set" was used.

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