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The Xclose™ Tissue Repair System is indicated for use in soft tissue approximation for procedures such as general and orthopedic surgery.

The modified Xclose™ Tissue Repair System consists of two (2) non-absorbable braided surgical 3-0 suture (Ultra High Molecular Weight Polyethylene, UHMWPE) and T-anchor (polyethylene terephthalate, PET) assemblies, connected together with a loop of green 2-0 suture (PET). The 2-0 suture loop is used to facilitate tightening, drawing the 3-0 suture/anchor assemblies together, thereby re-approximating the tissue. The suture components conform to USP requirements The construct is provided sterile and preloaded on a disposable delivery instrument.

The provided text describes a 510(k) submission for the Anulex Xclose™ Tissue Repair System. This is a medical device for soft tissue approximation, not an AI/ML powered device. Therefore, many of the requested categories related to AI/ML device performance evaluation (such as sample size for test/training sets, ground truth establishment for AI, MRMC studies, etc.) are not applicable to this document.

The document focuses on demonstrating substantial equivalence to a predicate device (K062307 - Xclose Tissue Repair System) through design control processes and specific testing, primarily tensile testing to confirm compliance with USP suture requirements.

Here's an attempt to answer the applicable questions based on the provided text, while clearly stating when information is not present or relevant to this type of device submission.

Acceptance Criteria and Device Performance for Xclose™ Tissue Repair System

1. A table of acceptance criteria and the 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 statement "tensile testing was performed" does not include details on the number of samples tested.
• Data Provenance: Not specified, but generally, such testing for regulatory submissions is conducted in-house by the manufacturer (Anulex Technologies, Inc. in Minnetonka, MN, USA) or by a contracted testing lab. This would be prospective testing as part of design verification.

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

• This question is not applicable as the device is not an AI/ML diagnostic or predictive tool, but rather a physical surgical device. The "ground truth" for its performance is objective mechanical testing (tensile strength, material properties) and verification of its physical function. There are no "experts" establishing a "ground truth" in the way a clinical image might be adjudicated.

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

• Not applicable. This device does not involve subjective assessment or interpretation of results by multiple experts that would require an adjudication method. Performance is measured against engineering specifications and USP standards.

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 is a physical surgical device, not an AI-powered diagnostic or assistive tool. MRMC studies are used for evaluating the performance of diagnostic devices, especially those involving human interpretation.

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

• Not applicable. This device does not contain an algorithm; it is a physical surgical instrument.

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

• The "ground truth" in this context refers to established engineering standards and material specifications, specifically "USP suture requirements." Performance is measured against these objective, pre-defined criteria. There is no biological "ground truth" (like pathology or outcomes data) being directly evaluated in this specific submission summary for the device modifications.

8. The sample size for the training set

• Not applicable. This is a physical device, not an AI/ML model that requires a training set.

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

• Not applicable. As there is no training set for an AI/ML model, the concept of establishing ground truth for it does not apply. The development of the device itself would follow established engineering design and manufacturing principles, with "ground truth" for its individual components being their adherence to material and mechanical specifications.

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