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510(k) Data Aggregation

    K Number
    K220199
    Date Cleared
    2022-03-21

    (56 days)

    Product Code
    Regulation Number
    888.3030
    Reference & Predicate Devices
    Why did this record match?
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    AFFINITY Proximal Tibia System is intended to treat fractures, nonunions of the proximal tibia including simple, comminuted, lateral wedge, depression, medial wedge, bicondylar combination of lateral wedge and depression, periprosthetic, and fractures with associated shaft fractures.

    • · Simple metaphyseal fractures (Classification AO 41-A2)
    • · Multifragmentary metaphyseal fractures (Classification AO 41-A3)
    • · Simple bicondylar fractures (Classification AO41-C1, 41-C2)
    • Multifragmentary bicondylar fractures (Classification AO 41-C3)
    • · Simple joint, simple metaphyseal fractures (Classification AO 41-C1)
    • · Diaphisary fractures (Classification AO 42A and 42B)
    Device Description

    AFFINITY Proximal Tibia System consists of anatomical plates and screws for the placement of the proximal tibial condyles, improving the restoration of the original structure. Similarly, in combination with the variable angle technique, it allows for the placement of screws in different configurations providing appropriate support for the correct healing of fractures.
    AFFINITY Proximal Tibia System consists of pre-contoured bone fixation plates and screws. The plates are made from biocompatible commercially pure titanium grade 4 according to ISO 5832-2 and ASTM F67 standard. The screws are made from biocompatible titanium alloy (Ti6Al4V) according to ISO 5832-3 and ASTM F136 standard.
    The AFFINITY Proximal Tibia System plates can be fixed with variable angle technique.

    AI/ML Overview

    The provided text is an FDA 510(k) summary for the AFFINITY Proximal Tibia System. This document describes a medical device (a bone plate and screw system for tibial fractures) and its claim of substantial equivalence to existing predicate devices.

    Crucially, the document states that "Clinical testing was not necessary for the substantial equivalence determination." This means that the device's acceptance criteria and the proof it meets them do NOT involve human clinical studies or AI-driven performance metrics such as accuracy, sensitivity, specificity, or human-in-the-loop improvements.

    Therefore, I cannot fulfill the request to describe the acceptance criteria and study that proves the device meets the acceptance criteria in the context of AI/machine learning performance. The provided text details non-clinical performance testing for a medical implant, focusing on mechanical, biocompatibility, and sterilization aspects.

    Here's why I cannot provide the requested information based on the given text:

    • No AI or algorithm present: There is no mention of any AI component, algorithm, or software in the device description or its testing.
    • No clinical performance data (e.g., accuracy, sensitivity): The document explicitly states "Clinical testing was not necessary." The performance testing described is mechanical (bend, torsion, pullout strength), biocompatibility, and sterilization, which are relevant for physical implants, not AI diagnostic or assistive devices.
    • No human reader studies (MRMC): Since no AI is involved, there are no studies assessing how human readers improve with AI assistance.
    • No ground truth establishment for diagnostic performance: The "ground truth" mentioned in your prompt (expert consensus, pathology, outcomes data) is typically for validating diagnostic or classification algorithms. For a bone plate, ground truth relates to its mechanical integrity and biological safety, which were assessed through laboratory tests.

    In summary, the provided document describes the regulatory approval of a physical medical device (a bone plate and screw system) through established non-clinical testing and comparison to predicates, not the validation of an AI/ML medical device.

    If you have a document describing an AI/ML medical device's performance study, I would be happy to analyze it according to your detailed criteria.

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