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Elbow: Biceps Tendon Reattachment, Ulnar or Radial Collateral Ligament 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, Carpal 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 Ligament Repair, Patellar Tendon Repair, Posterior Oblique Ligament Repair, Iliotibial Band Tenodesis

Hip: Capsular Repair, Acetabular Labral Repair, Gluteal Tendon Repair

The ICONIX TT All Suture Anchors are intended for single use only.

The Stryker ICONIX TT All Suture Anchors are a line extension of the legally marketed Stryker ICONIX All Suture Anchors. The ICONIX TT All Suture Anchors are soft-tissue fixation devices with a push-in design, provided preloaded on a disposable inserter. They are composed of a sheath structure that contains one or more working sutures. The sheath bunches as the anchor is deployed to fixate in bone.

The provided text is related to a 510(k) premarket notification for a medical device called the "Stryker ICONIX TT All Suture Anchor." This document describes the device's technical characteristics, its intended use, and a comparison with a predicate device to establish substantial equivalence.

Based on the information provided, no acceptance criteria or studies proving a device meets acceptance criteria related to AI/algorithm performance are present. This document is for a physical medical device (suture anchor) and the "performance data" section refers to non-clinical verification and validation testing for mechanical properties and biocompatibility, not an AI algorithm.

Therefore, I cannot fulfill your request to describe acceptance criteria and a study proving an AI device meets them using this input. The questions you've asked (about sample sizes, ground truth, experts, MRMC studies, standalone performance) are specific to the evaluation of AI/ML-driven medical devices, which is not the subject of this document.

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