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The Titanium Suture Anchor is intended to be used for suture or tissue fixation in the foot, ankle, knee, hip, hand, wrist, elbow and shoulder. Specific indications are listed below:

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 Reconstruction

Hand/Wrist: Scapholunate 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 transfers

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

The Titanium Suture Anchor is a titanium alloy threaded device designed to attach soft tissues to bone when used in conjunction with suture.

This is a 510(k) summary for a medical device called the Titanium Suture Anchor. It primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with acceptance criteria in the typical sense of diagnostic or AI-driven devices.

Here's a breakdown of the information provided, tailored to your request, but acknowledging the nature of this particular submission:

1. Table of Acceptance Criteria and Reported Device Performance

For this specific device (Titanium Suture Anchor), the "acceptance criteria" are related to mechanical performance and demonstrating equivalence to a predicate device. The document does not describe a clinical study comparing the device against specific clinical outcome metrics with statistical acceptance thresholds as one might see for a diagnostic tool or an AI algorithm. Instead, it relies on nonclinical mechanical testing.

Study Proving Device Meets Acceptance Criteria:

The study proving the device meets its acceptance criteria is a nonclinical testing program.

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

• Sample Size: Not explicitly stated as a numerical value (e.g., N=X units). The document states, "All testing was performed on test units representative of finished devices." This implies multiple units were tested for each type of nonclinical test.
• Data Provenance: Not applicable in the traditional sense of patient data. The "data" comes from laboratory mechanical testing of manufactured device units. It is not patient-derived or clinical.

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

Not applicable. For mechanical tests of a physical device, ground truth is established by engineering standards, test methods, and measurement equipment, rather than expert interpretation of data. The expertise resides in the engineers and technicians performing and validating the tests.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods like 2+1 or 3+1 are used for human interpretation discrepancies, typically in clinical readings or image analysis. Mechanical testing results are objective measurements.

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 medical device (suture anchor), not an AI diagnostic or assistive technology. No MRMC study was conducted or is relevant for this type of 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. There is no algorithm to evaluate.

7. The Type of Ground Truth Used

The "ground truth" for the nonclinical tests is based on engineering specifications, established test methods, and measurable physical properties deemed acceptable for a device of its type and in comparison to its predicate device. This is the closest equivalent to ground truth for this kind of submission.

8. The Sample Size for the Training Set

Not applicable. This device is not an AI algorithm, so there is no training set in the machine learning sense. The "design" of the device is developed through engineering principles, prototyping, and iterative testing, not through a data-driven training process.

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

Not applicable, as there is no training set for an AI algorithm. The device's design is validated against engineering requirements and established safety/performance standards for similar devices.

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