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510(k) Data Aggregation

    K Number
    K221099
    Manufacturer
    Date Cleared
    2022-05-13

    (29 days)

    Product Code
    Regulation Number
    888.3030
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Arthrex Self-Punching PushLock Suture Anchor

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Arthrex self-punching PushLock suture anchors are intended to be used for suture (soft tissue) fixation to bone in the shoulder: Shoulder: Rotator Cuff Repair

    Device Description

    The Arthrex Self-Punching PushLock Anchor is a push-in, fully threaded barbed suture anchor comprised of a PEEK Optima eyelet and a hollow anchor body pre-assembled on a disposable inserter. The anchor body is made of either PLLA/βTCP or PEEK Optima. The Arthrex Self-Punching PushLock Anchor is intended to be used for suture (soft tissue) fixation to bone in the shoulder.

    AI/ML Overview

    The provided FDA 510(k) summary is for a medical device called the "Arthrex Self-Punching PushLock Suture Anchor". This document does not include information about AI/ML device performance or clinical studies in the context of diagnostic accuracy, which is what your questions are geared towards.

    The performance data summarized in this document relates to the mechanical properties, shelf-life, MR safety, and pyrogenicity of a physical medical device (suture anchor), not the performance of an AI model. Therefore, I cannot extract the information required for your questions (acceptance criteria, study design for AI models, expert ground truth, etc.) from this document.

    The document states:

    • Performance Data: "Ultimate load testing and insertion testing was performed on the subject device and compared to the predicate device cleared under K101679 to demonstrate that the modifications do not negatively impact mechanical strength." It also mentions shelf-life and MR safety.

    This means the acceptance criteria and study proving it, in this context, would be based on engineering and biocompatibility standards, not on diagnostic accuracy metrics like sensitivity, specificity, or AUC as evaluated for AI models.

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