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510(k) Data Aggregation

    K Number
    K202971
    Date Cleared
    2021-05-06

    (218 days)

    Product Code
    Regulation Number
    888.3030
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The DRPx Locking Distal Radius Plate System is indicated for the fixation of intra- and extra-articular fractures and osteotomies of the distal radius.

    Device Description

    The DRPx Locking Distal Radius Plate System consists of titanium plates for the distal radius, bone peos, bone screws and instruments to facilitate implantation. The Distal Radius plates come in a variety of sizes and are precontoured to match the anatomy of the distal radius. They accept 2.0mm locking screws, and 2.6mm and 3.5mm locking and non-locking bone screws. The bone screws range in length from 6mm to 30mm. The system also includes instruments used to implant the plates.

    AI/ML Overview

    This a 510(k) premarket notification for the DRPx Locking Distal Radius Plate System. The document focuses on demonstrating substantial equivalence to previously cleared devices rather than providing detailed acceptance criteria and a study to prove meeting those criteria in the context of an AI/ML device.

    Therefore, this document does not contain the information requested regarding acceptance criteria for an AI/ML device and a study proving the device meets those criteria. The provided text describes the regulatory clearance of an orthopedic implant system and focuses on its mechanical properties and equivalence to predicate devices, not on AI/ML performance.

    Specifically, the document refers to:

    • Performance Testing: Single cycle bend testing and bending fatigue testing performed on the plates per ASTM F382.
    • Conclusion: The device was found to have acceptable mechanical characteristics for its intended uses.

    This type of testing is relevant for the mechanical integrity of an implant, not for the performance metrics typically associated with AI/ML systems (like sensitivity, specificity, AUC, etc.).

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