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510(k) Data Aggregation

    K Number
    K960087
    Date Cleared
    1996-09-20

    (253 days)

    Product Code
    Regulation Number
    888.3160
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    SORIBIE RESURFACING TOTAL ELBOW SYSTEM (PROPOSED NAME)

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

    The Sorbie Resurfacing Total Elbow System is indicated for use in total clbow arthroplasty for reduction or relief of pain and/or improved elbow function in skeletally mature patients with the following conditions: 1) noninflammatory degenerative joint disease including osteoarthritis or traumatic arthritis; 2) inflammatory degenerative joint disease including rheumatoid arthritis; 3) correction of functional deformity; 4) revision procedures where other treatments and devices have failed; and 5) treatment of fractures that are unmanageable using other techniques.

    Device Description

    The Sorbie Resurfacing Total Elbow System consists of humeral, ulnar, and radial head components. Each component is available in a range of sizes to fit varying anatomical requirements.

    AI/ML Overview

    This document describes a 510(k) summary for the Sorbie Resurfacing Total Elbow System, a medical device for total elbow arthroplasty. The provided text is a premarket notification for a medical device and describes its components, intended use, and materials, along with a "Testing Summary" table demonstrating some performance characteristics. However, it does not include information about acceptance criteria and a study proving the device meets those criteria in the context of an AI/ML powered device, which is implied by the structure of the prompt.

    Therefore, many of the requested details cannot be extracted from the provided text because they pertain to AI/ML device validation, which is not relevant to this traditional medical device submission.

    Here's a breakdown of what can be extracted and what cannot:

    1. A table of acceptance criteria and the reported device performance

    The document provides a "Testing Summary" table, which lists mechanical tests and their results. These results implicitly serve as the "reported device performance" against some unstated acceptance criteria. The acceptance criteria themselves (e.g., minimum force values, stress limits) are not explicitly enumerated in a separate column as "acceptance criteria."

    Test DescriptionReported Device Performance
    Failure force analysis of the humeral componentCompressive loading will not result in failure of the component if bony support exists.
    Stress analysis of the stemmed humeral componentComponent exhibits peak stresses comparable to other devices and slightly over the fatigue strength of the material for joint forces typical of normal elbow joints. (Note: "slightly over the fatigue strength" could be interpreted as a potential concern depending on the actual values and safety margins, but the document presents it as a result).
    Stress analysis of the ulnar componentWhen fully supported by bone, the component exhibits stresses well within the fatigue strength of the material.
    Failure force analysis of the radial head componentAll sizes demonstrate material strength properties suitable for this application.
    Ulnar component lock detail testingAny extraction forces will be countered by compressive loading placed on the components by the humerus.
    Properties of the UHMWPEThe tensile properties of the test parts meet or exceed the minimum requirements by ASTM F 648.

    Missing Information (Not applicable to this document as it's not an AI/ML device):

    • Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
    • Number of experts used to establish the ground truth for the test set and the qualifications of those experts
    • Adjudication method for the test set
    • If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
    • If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
    • The type of ground truth used (expert consensus, pathology, outcomes data, etc)
    • The sample size for the training set
    • How the ground truth for the training set was established
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