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The Tornier Insite FT Suture Anchor is intended for fixation of soft tissue to bone. The Tornier Insite FT Suture Anchor is intended for use in the following applications: Shoulder: Rotator Cuff, Bankart and SLAP lesion repair, Biceps tenodesis, Acromio-Clavicular separation and Deltoid repair, Capsular shift and Capsulolabral reconstruction. Foot/Ankle: Lateral and Medial stabilization, Achilles tendon and Metatarsal ligament repair, Hallux Valgus and Midfoot Reconstruction. Knee: Medial collateral and Lateral collateral ligament repair, Patellar tendon and Posterior oblique ligament repair, Illiotibial band tenodesis. Hand/Wrist: Scapholunate ligament, Radial collateral ligament and Ulnar collateral ligament reconstruction. Elbow: Biceps tendon reattachment, Tennis elbow repair, Ulnar and Radial collateral ligament reconstruction.

The Tornier Insite FT Titanium Suture Anchor w/ Needles consists of a bone implant device intended for the fixation of soft tissue to bone. The device is a fully threaded titanium suture anchor preloaded on a disposable inserter assembly, attached USP size#2-0 or USP size #0 UHMWPE suture and comes with needles attached to the ends of the suture. The Tornier Insite FT Titanium Suture Anchor w/ Needles is used as a means for securing soft tissue to bone. The implant is individually packaged and sterilized though ethylene oxide (EO) using appropriate standard and guidelines

This is a medical device 510(k) summary for the Tornier Insite FT Suture Anchor with Needles, not an AI/ML device. Therefore, much of the requested information regarding AI/ML device performance, such as sample sizes for test and training sets, number and qualifications of experts, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for training data, is not available or applicable.

However, based on the provided document, I can extract information related to the acceptance criteria and study for this medical device.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document states "All tests met the pre-established acceptance criteria," but does not explicitly detail the quantitative values of these criteria. It implies that for each listed test, a specific performance benchmark was set, and the device successfully achieved or exceeded that benchmark.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size: Not explicitly stated. The document mentions "Non-clinical laboratory testing was performed" and "cadaveric laboratory simulated use testing," implying a physical testing methodology rather than a data-driven test set in the context of AI/ML. The number of physical units or cadavers used is not specified.
• Data Provenance: The testing was "non-clinical laboratory testing" and "cadaveric laboratory simulated use testing." This indicates the data provenance is from laboratory experiments, likely conducted in a controlled environment. The country of origin for the cadavers or the testing facility is not specified.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• This question is not applicable as this is a physical medical device, not an AI/ML diagnostic or predictive tool that requires expert-established ground truth for a test set. The "ground truth" for this device would be its physical and mechanical performance characteristics against engineering and safety standards.

4. Adjudication Method for the Test Set

• This question is not applicable for the same reasons as point 3. Adjudication methods like 2+1 or 3+1 are used for reconciling discrepancies in expert opinions on diagnostic images or data, which is not relevant here.

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 question is not applicable as this is a physical medical device (suture anchor), not an AI/ML system designed to assist human readers (e.g., radiologists).

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• This question is not applicable as this is a physical medical device, not an algorithm. The "standalone performance" is simply the device's inherent mechanical and functional performance, which was evaluated through the non-clinical laboratory tests.

7. The Type of Ground Truth Used (Expert Consensus, Pathology, Outcomes Data, etc.)

• The "ground truth" for this medical device's performance is based on engineering specifications, biomechanical properties, and safety standards relevant to bone fixation devices. The document indicates that tests were performed "per design requirements and risk analysis." This would include metrics like failure strength, pullout strength, torque, and suture integrity, which are quantifiable engineering parameters.

8. The Sample Size for the Training Set

• This question is not applicable as this is a physical medical device and does not involve a "training set" in the context of AI/ML. Device design and manufacturing processes are refined through engineering, material science, and iterative testing, not by training an algorithm on a dataset.

9. How the Ground Truth for the Training Set Was Established

• This question is not applicable as there is no "training set" or corresponding "ground truth" in the AI/ML sense for this physical medical device. The "ground truth" for the device's development would be established through established engineering principles, material science knowledge, and clinical requirements for soft tissue-to-bone fixation.

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