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The Titan Ti Suture Suture Anchor is intended for fixation of suture (soft tissue) to bone in the shoulder, foot/ankle, hip, knee, hand/wrist and elbow in the following procedures:

• Shoulder: Rotator Cuff Repairs, Bankart Repair, SLAP Lesion Repair, Biceps Tendodesis, Acromio-Clavicular Separation Repair, Deltoid Repair, Capsular Shift or Capsulolabral Reconstruction;
• Foot/Ankle: Lateral Stabilization, Medial Stabilization, Achilles Tendon Repair, Hallux Valgus Reconstruction, Midfoot Reconstruction, Metatarsal Ligament/Tendon Repair, Bunionectomy;
• Knee: Anterior Cruciate Ligament Repair, Medial Collateral Ligament Repair, Lateral Collateral Ligament Repair, Patellar Tendon Repair, Posterior Oblique Ligament Repair, and Illiotibial Band Tenodosis;
• Hand/Wrist: Scapholunate Ligament Reconstruction, Ulnar Collateral Ligament Reconstruction, Radial Collateral Ligament Reconstruction;
• Elbow: Biceps Tendon Reattachment, Tennis Elbow Repair, Ulnar or Radial Collateral Ligament Reconstruction, Lateral Epicondylitis repair;
• Hip: Capsular Repair, Acetabular Labral Repair

The Titan Ti Suture Anchor is a fully-threaded, self-tapping, titanium corkscrew shape anchor available in 5.5mm and 6.5mm diameter sizes. The suture anchor comes preconfigured with MagnumWire® sutures and is mounted on a disposable delivery driver. The device is supplied sterile and is available with or without needles.

The provided text describes a 510(k) summary for the ArthroCare® Corporation Titan™ Ti Suture Anchor. This document details the device's substantial equivalence to predicate devices, focusing on physical and mechanical performance testing rather than clinical study data involving human or expert-derived diagnoses.

Therefore, many of the requested fields regarding acceptance criteria, study design, and ground truth in the context of diagnostic or AI performance are not applicable to this type of device submission. The submission relies on establishing substantial equivalence through engineering and material performance comparison and does not involve AI or diagnostic interpretation.

However, I can extract the information relevant to the types of studies mentioned and present it in the requested format.

Acceptance Criteria and Device Performance for ArthroCare® Corporation Titan™ Ti Suture Anchor

The provided 510(k) summary for the Titan™ Ti Suture Anchor does not detail acceptance criteria and device performance in the context of diagnostic accuracy or AI performance. Instead, it focuses on establishing substantial equivalence to predicate devices through performance testing that demonstrates the physical and mechanical properties of the suture anchor are comparable.

The "performance testing" referred to in the document would typically involve mechanical tests to assess properties like pull-out strength, fatigue, and material biocompatibility, rather than diagnostic accuracy metrics like sensitivity or specificity. The acceptance criteria would be defined as meeting or exceeding the performance characteristics of the predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

As specific numerical acceptance criteria (e.g., minimum pull-out strength in Newtons) and corresponding device performance values are not explicitly stated in the provided text, a detailed table cannot be populated. The document only generically states:
"Additionally, performance testing has been completed to demonstrate the substantial equivalence of the Titan Ti Suture Anchor to the predicate device."
"The differences in performance specifications, and labeling are not substantial changes or modifications, and do not significantly affect the safety or efficacy of the device."

Therefore, the principle acceptance criterion is:

• Acceptance Criterion: The Titan Ti Suture Anchor's performance specifications (likely mechanical and material properties) must be substantially equivalent to those of the predicate devices.
• Reported Device Performance: Performance testing demonstrated substantial equivalence to predicate devices.

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

• Sample Size: Not specified in the provided text. The "performance testing" likely involved a set of physical devices subjected to mechanical tests.
• Data Provenance: Not specified. This would refer to laboratory testing data, not clinical patient data.

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

• Not Applicable. For this type of medical device (suture anchor), "ground truth" is established through engineering and material science testing standards, not through expert human interpretation or diagnosis.

4. Adjudication Method for the Test Set

• Not Applicable. Adjudication methods (e.g., 2+1, 3+1) are relevant for studies involving human interpretation or clinical outcomes where discrepancies need resolution. This is not the case for mechanical performance testing of a suture anchor.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• Not Performed/Not Applicable. The device is a physical implant, not a diagnostic tool or AI-assisted system for interpretation. MRMC studies analyze human reader performance, usually in radiology or pathology, which is irrelevant to this device.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Not Performed/Not Applicable. This device is a physical medical implant, not an algorithm or AI system. Therefore, standalone performance in the context of AI is not relevant.

7. Type of Ground Truth Used

• Engineering and Material Science Standards: The "ground truth" for this device's performance would be defined by industry standards for mechanical properties (e.g., ASTM standards for pull-out strength, fatigue) and biocompatibility, as measured by laboratory testing. It is not expert consensus, pathology, or outcomes data in the clinical sense.

8. Sample Size for the Training Set

• Not Applicable. There is no "training set" in the context of AI or machine learning for this physical medical device. The "training" in manufacturing involves process validation and quality control, not data-driven model training.

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

• Not Applicable. As there is no training set as understood in AI/ML, there's no ground truth established for it.

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