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The Tigon Medical All-Suture Anchors are intended for the reattachment of soft tissue to bone for the following indications:
• Elbow: Biceps Tendon Reattachment. Ulnar or Radial Collateral Liqament Reconstruction
• Shoulder: Rotator Cuff Repair, Bankart Repair, SLAP Lesion Repair, Biceps Tenodesis, Acromio-Clavicular Separation Repair, Deltoid Repair, Capsular Shift or Capsulolabral Repair
• Hand/Wrist: Scaphulolunate Ligament Reconstruction, Carnal Ligament Reconstruction, Repair/ Reconstruction of Collateral Ligaments, Repair of Flexor and Extensor Tendons at the PIP, DIP and MCP Joints for all Digits, Digital Tendon Repair
• Foot/Ankle: Lateral Stabilization, Medial Stabilization, Achilles Tendon Repair, Metatarsal Ligament Repair, Hallux Valgus Reconstruction, Digital Tendon Transfers, Mid-foot Reconstruction
• Knee: Medial Collateral Ligament Repair, Lateral Collateral Ligament Repair, Patellar Tendon Repair, Posterior Oblique Ligament Repair, Iliotibial Band Tenodesis
• Hip: Capsular Repair, Acetabular Labral Repair, Gluteal Tendon Repair

The Tigon Medical All-Suture Anchors are soft-tissue fixation devices provided sterile, preloaded on an inserter. The anchor consists of different load configurations consisting of one or more working sutures. The inserters can be reprocessed after use and are made from stainless steel.

This document is a 510(k) premarket notification for a medical device (Tigon Medical All-Suture Anchors). It does not include information about AI/ML device performance or clinical studies with human readers. Therefore, I cannot extract the requested information regarding acceptance criteria, study details, sample sizes for test/training sets, or expert qualifications for ground truth establishment.

The document discusses the substantial equivalence of the device to legally marketed predicate devices based on mechanical testing, specifically according to ASTM F543. The acceptance criteria and "performance" in this context relate to the physical and mechanical properties of the anchor itself, not the performance of an AI/ML algorithm.

Here's the information that can be extracted or inferred from the provided text, related to the device's assessment:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance:
Not applicable for an AI/ML study. The "test set" here refers to the samples of the medical device used for mechanical testing. The specific sample size for the mechanical testing is not explicitly stated in this document. Data provenance would refer to the origin of the mechanical test results.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
Not applicable as this is not an AI/ML study involving human interpretation of medical images or data. Ground truth in this context would be established by the mechanical testing protocols and measurements.

4. Adjudication method for the test set:
Not applicable as this is not an AI/ML study requiring expert adjudication of results. Mechanical testing results are typically evaluated against pre-defined engineering specifications.

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, an MRMC comparative effectiveness study was not done. This document pertains to the clearance of a physical medical device (all-suture anchors), not an AI/ML-powered diagnostic or assistive tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
No, standalone algorithm performance was not done. This document is not about an algorithm.

7. The type of ground truth used:
The "ground truth" for the device's performance is established through mechanical testing (per ASTM F543) comparing the device's physical properties (e.g., insertion strength, fixation strength) to those of predicate devices.

8. The sample size for the training set:
Not applicable, as this is not an AI/ML study.

9. How the ground truth for the training set was established:
Not applicable, as this is not an AI/ML study.

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