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510(k) Data Aggregation

    K Number
    K173443
    Date Cleared
    2018-02-01

    (87 days)

    Product Code
    Regulation Number
    878.4750
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K061095, K171589

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

    The AEON™ Endoscopic Stapler has applications in general, abdominal, gynecologic, pediatric, and thoracic surgery for resection, transection, and creation of anastomoses.

    Device Description

    The AEON™ Endoscopic Stapler places two. triple-staggered rows of titanium staples and simultaneously divides the tissue from a central line. The size of the staples is determined by the selection of the appropriate reload.

    This 510(k) reports reloads with two staple sizes (gray - 2.25mm and black - 5.0mm) and in two lengths (45mm and 60mm).

    AI/ML Overview

    The provided document is a 510(k) premarket notification summary for the AEON™ Endoscopic Stapler. It focuses on demonstrating substantial equivalence to a predicate device through non-clinical performance testing. It does not contain information about an AI/ML-driven medical device, nor does it detail a study involving human readers, ground truth establishment by experts, or any of the criteria typically associated with the evaluation of AI systems in medical imaging.

    Therefore, I cannot provide a response filling out the requested table and details because the document is about a surgical stapler and its mechanical/physical performance, not an AI software.

    The document explicitly states: "This submission does not include data from Clinical Studies." and the performance data section lists purely mechanical and material tests (e.g., Firing Force, Staple Formation, Burst Evaluation, Biocompatibility).

    To clarify, a medical device like the AEON™ Endoscopic Stapler (a physical surgical tool) would not typically have acceptance criteria, performance metrics, or study methodologies that involve:

    • A "test set" or "training set" of data points in the sense of AI/ML.
    • "Experts" establishing "ground truth" for image interpretation.
    • "Adjudication methods" for discrepant reads.
    • "Multi-reader multi-case (MRMC) comparative effectiveness studies" for human readers improving with AI assistance.
    • "Standalone (algorithm only without human-in-the-loop performance)" studies.

    The document's purpose is to show the stapler performs its mechanical function safely and effectively, similar to an existing predicate device.

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