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510(k) Data Aggregation

    K Number
    K033202
    Device Name
    ADVANTAGE PADDLE
    Date Cleared
    2003-12-03

    (62 days)

    Product Code
    Regulation Number
    892.1710
    Reference & Predicate Devices
    Why did this record match?
    Applicant Name (Manufacturer) :

    LIVINGSTON PRODUCTS, INC.

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

    For use in Screening Mammographic Imaging as a Specialty Paddle for Full Breast Compression as an alternative to conventional breast compression paddles.

    Device Description

    The Advantage paddle is a breast compression paddle comprising of a rectangular tray-like structure of an FDA approved biocompatible material (Eastman Kodak PETG Copolyester #6763). The paddle has a flat section and a tilted section made possible by two-section construction. The first section of the paddle is foamed rigidly and is positioned near the patient's chest and extends about one inch away from the chest. In operation, this provides a vertically oriented force to the breast that tends to keep the breast tissue from being forced into the chest wall and provides a clearer film of the breast tissue adjacent the chest. The scond section is formed and structured to flex for the purpose of tilting the paddle downward to compress the nipple end of the breast in a steady position as the X-ray is taken. The "Advantage" paddle is mcant for use with an 18 x 24cm bucky. That is a standard size used in the industry

    AI/ML Overview

    The provided document is a 510(k) summary for a breast compression paddle, not an AI/ML device. Therefore, it does not contain information related to acceptance criteria, device performance, study details, or ground truth establishment relevant to an AI/ML device.

    The document discusses the substantial equivalence of the "Advantage Breast Compression Paddle" to a predicate device ("S.O.F.T. Paddle") based on design, materials, indications for use, and a comparison of construction and images. There is no performance data in the context of diagnostic accuracy, sensitivity, specificity, etc., that would be applicable to an AI/ML algorithm.

    Therefore, I cannot provide the requested information for an AI/ML device based on this document.

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