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The Parcus GFS BTB are indicated for use in the fixation of ligaments and tendons in patients requiring ligament or tendon repair.

The Parcus GFS BTB is designed for use in the fixation of ligaments and tendons in patients requiring ligament or tendon repair. The devices are made from medical grade titanium and UHMWPE suture and are intended on being used in conjunction with other Parcus Medical devices such as the GFS Ultimate, GFS Mini and GFS II. The GFS BTB is provided sterile.

The provided text does not contain information about the acceptance criteria or a study proving that a device meets such criteria in the context of an AI/ML-based medical device.

The document is a 510(k) premarket notification for a medical device called "Parcus GFS BTB," which is a device for the fixation of ligaments and tendons. It discusses the device's substantial equivalence to predicate devices, its indications for use, and a summary of performance data related to its mechanical properties (strength and elongation under cycle loading and ultimate failure conditions).

Therefore, I cannot extract the requested information regarding AI/ML device performance, acceptance criteria, sample sizes, expert ground truth, MRMC studies, or standalone algorithm performance.

Here's why the document doesn't contain the requested information:

• Device Type: The "Parcus GFS BTB" is a mechanical implant (bone fixation fastener), not an AI/ML-based diagnostic or therapeutic device.
• Performance Data: The performance data described relates to the physical and mechanical properties of the implant (strength, elongation, pyrogenicity), not to diagnostic accuracy, sensitivity, specificity, or other metrics typically associated with AI/ML models.
• Regulatory Pathway: The 510(k) pathway for this device focuses on substantial equivalence to existing predicate devices, primarily through comparison of materials, design, indications for use, and mechanical performance. It does not involve the types of clinical studies, ground truth establishment, or human-in-the-loop assessments common in AI/ML device submissions.

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