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The Bio-Absorbable Corkscrew Suture Anchor is intended for fixation of soft tissue to bone. This product is intended for the following indications:

Shoulder: Rotator Cuff Repair, Bankart Repair, SLAP Lesion Repair, Biceps Tenodesis, 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 Repair Knee: Medial Collateral Ligament Repair, Lateral Collateral Ligament Repair, Patellar

Tendon Repair, Posterior Oblique Ligament Repair, Iliotibial Band Tenodesis Hand/Wrist: Scapholunate Ligament Reconstruction, Ulnar Collateral Ligament Reconstruction, Radial Collateral Ligament Reconstruction

Elbow: Biceps Tendon Reattachment, Tennis Elbow Repair, Ultar or Radial Collateral Ligamant Reconstruction

Pelvis: Bladder Neck Suspension for female urinary incontinence due to urethral hypermobility or intrinsic sphincter deficiency

The Bio-Absorbable Corkscrew Suture Anchor is manufactured using poly (L, DL-lactide). It is a threaded design with two strands of #2 braided polyester suture insert molded through the center of the device. The anchor has a hex head, which is seated in a disposable driver for insertion purposes. Prior to driving in the anchor, it is necessary to prepare the bone using a tap. Once in place, the free ends of the suture are used to reattach soft tissue to the bone.

This document is a 510(k) Summary for a medical device called the "Arthrex Bio-Absorbable Corkscrew Suture Anchor." It is a premarket notification to the FDA to demonstrate substantial equivalence to a legally marketed predicate device.

Crucially, this document describes a physical medical device (suture anchor) and its intended use, not an AI/ML-driven software device. Therefore, the requested information regarding acceptance criteria, study details, ground truth, and AI/ML specific aspects (like MRMC studies, standalone performance, training sets, etc.) is not applicable to this submission.

The 510(k) summary focuses on demonstrating that the Bio-Absorbable Corkscrew Suture Anchor is substantially equivalent to predicate devices based on its intended use and technological characteristics, and that it doesn't raise new questions of safety and effectiveness.

Here's why the AI/ML-specific questions are not applicable:

• No AI/ML Component: The device described is a physical implant made of poly (L, DL-lactide) with braided polyester suture. It does not involve any software, algorithms, or machine learning.
• Substantial Equivalence Pathway: The 510(k) pathway for this type of device primarily involves demonstrating equivalence to existing legally marketed devices, rather than establishing de novo safety and effectiveness through clinical trials with specific performance metrics akin to AI/ML diagnostics.
• Lack of Performance Metrics: The document does not describe performance metrics like sensitivity, specificity, accuracy, or AUC, which are common for AI/ML devices. Instead, it refers to the material being "well characterized" and having been used in a predicate device.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, or ground truth types in the context of an AI/ML device, as this information is not present nor relevant to the provided text.

The document states:

• "The differences between the Bio-Absorbable Corkscrew and the predicate devices cited do not raise any different questions regarding safety and effectiveness."
• "Furthermore, the material is well characterized, and has been used in a predicate device with a similar indication."
• "The device, as designed, is as safe and effective as the predicate devices."

This indicates that the "acceptance criteria" for this 510(k) was demonstrating substantial equivalence to predicate devices based on material characteristics, design, and intended use, rather than specific quantitative performance metrics derived from a study like one would describe for an AI/ML product.

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