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510(k) Data Aggregation

    K Number
    K152662
    Device Name
    S-Wire
    Date Cleared
    2016-03-09

    (174 days)

    Product Code
    Regulation Number
    888.3040
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The S-Wire is intended to perform as temporary fixation and stabilization unit of bone fractures and as guidance at insertion of implants into the skeletal system.

    Device Description

    The S-Wire is composed of wires manufactured from stainless steel per ASTM F138 and nitinol per ASTM F2063. The wires are available in lengths of 150-550mm and diameters of .7-1.6mm. The S-wire is available with the following tip styles: Diamond Smooth, Diamond Threaded, Round Smooth, Round Threaded, Trocar Smooth, Trocar Threaded, and Diamond Suture Passer.

    AI/ML Overview

    This is a 510(k) premarket notification for a medical device called "S-Wire." The document does not contain information about an AI/ML device or a study involving acceptance criteria for such a device.

    The provided text describes a traditional medical device (fixation pins/wires) and its substantial equivalence to a predicate device based on material, design, and intended use. Therefore, I cannot extract the requested information about acceptance criteria or studies related to AI/ML device performance.

    Here's why the requested information cannot be provided from this document:

    • No AI/ML Device: The S-Wire is a physical orthopedic device made of stainless steel and nitinol. It is not an AI/ML software or system.
    • No Performance Study as Described: The 510(k) submission establishes substantial equivalence to a predicate device. This typically involves demonstrating similar design, materials, manufacturing, intended use, and performance characteristics (often through bench testing or literature review), not complex clinical trials or detailed performance metrics against acceptance criteria in the way AI/ML algorithms are evaluated.
    • Lack of Specific Data: There is no mention of sample sizes for test sets, data provenance, expert ground truth establishment, MRMC studies, standalone performance, training sets, or how ground truth for training sets was established, as these are not relevant to the type of device and submission described.
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