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510(k) Data Aggregation

    K Number
    K131552
    Device Name
    CONVENTUS DRS
    Date Cleared
    2014-02-12

    (258 days)

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

    CONVENTUS DRS

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

    The Conventus DRSTM is intended for the fixation of distal radius fractures.

    Device Description

    The Conventus DRSTM is an intramedullary device intended to treat distal radius fractures. The DRS is comprised of an Expandable Scaffold, Fragment Screws, and a Proximal Plate. The device remains flexible during placement, but is made rigid at the completion of the surgical implant procedure. The implant is made from titanium alloy (Ti6Al4V) and Nitinol.

    AI/ML Overview

    The provided text is a 510(k) summary for the Conventus DRS™ (Distal Radius System) device, which is a fracture fixation device. The document describes the device, its intended use, and its substantial equivalence to a previously cleared device. However, it does not contain information about acceptance criteria, device performance metrics, or any studies with AI involvement.

    The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (K102689) based primarily on the device's design, materials (titanium alloy and Nitinol), and intended use for distal radius fractures. The only "testing" mentioned is for "MR Conditional" labeling, which assesses safety in a magnetic resonance environment, not clinical performance for fracture fixation.

    Therefore, I cannot populate the table or answer the questions as the required information (acceptance criteria, performance data, details of a study proving criteria, sample sizes, ground truth establishment, AI involvement, etc.) is not present in the provided document.

    The document is a regulatory submission for a traditional medical device (fracture fixation) and does not appear to involve any AI/ML components for diagnosis, prediction, or intervention that would require performance studies against specific acceptance criteria in the manner described in your request.

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