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510(k) Data Aggregation
(149 days)
Button Loop
The Button Loop is used for the fixation of soft tissue to bone in orthopedic procedures such as ACL repair.
The proposed device, Button Loop is a machined titanium implant designed to provide fixation in the repair of tendons and ligaments. It consists of a titanium implantable button with a pre-attached loop. This implantable loop has non-absorbable suture attached to the button for assisting in the button placement and is discarded after the device placement. The button is offered in 12mm one size and loop is available in size ranges of 10~60mm in 5mm increments to accommodate different graft sizes.
This FDA 510(k) premarket notification for the "Button Loop" device does not contain information about acceptance criteria or a study proving that the device meets those criteria in the context of an AI/ML algorithm.
The document describes a medical device called the "Button Loop," which is a metallic bone fixation fastener used for soft tissue to bone fixation in orthopedic procedures like ACL repair. The submission is focused on demonstrating substantial equivalence to a predicate device (RIGIDLOOP Cortical Fixation System) based on non-clinical tests (biocompatibility, mechanical tests, sterilization) and technological characteristics.
Therefore, I cannot provide the requested information about acceptance criteria, device performance, sample sizes for test sets, data provenance, number or qualifications of experts, adjudication methods, MRMC studies, standalone performance, ground truth types, or training set details, as this device is not an AI/ML powered device, nor does the document describe such a study.
The "Non-Clinical Test Conclusion" (Section 6) and "Table 1 Comparison of Technology Characteristics" (Section 7) of the 510(k) summary are the closest to performance evaluation in this document. They cover aspects like mechanical strength, biocompatibility, and sterilization, but these do not involve the types of acceptance criteria and studies typically associated with AI/ML device performance.
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