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510(k) Data Aggregation

    K Number
    K211072
    Date Cleared
    2021-04-27

    (15 days)

    Product Code
    Regulation Number
    876.5980
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Safety Trocar Cannula is intended to be used for guidewire introduction during gastrointestinal procedures such as PEG (percutaneous Endoscopic Gastrotomy), PEJ (Percutaneous Endoscopic Jejunostomy) or other endoscopic gastrointestinal procedures requiring placement of a guidewire.

    Device Description

    The Safety Trocar Cannula is a sterile, single used for guidewire introduction during a gastrointestinal procedure. The device consists of a safety trocar with passive sharps protection and a cannula.

    During a gastrointestinal procedure requiring access via a guidewire, the device is inserted into the stomach under direct visualization via an endoscope. Once inserted, the safety trocar is removed from the cannula that provides smooth surface for the needle to slide out during removal as well as a lumen to facilitate guidewire introduction. The passive safety feature of the proposed Safety Trocar Cannula is activated when the safety trocar is separated from the cannula.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for a medical device called "Safety Trocar Cannula." It details the device's intended use, technological characteristics, and evidence for substantial equivalence to a predicate device. However, it does not include information about acceptance criteria or a study proving that a device meets those acceptance criteria in the context of an AI/ML algorithm.

    The document is for a physical medical device, specifically a "Safety Trocar Cannula," and the performance data presented is for benchtop testing, biocompatibility, and sterilization testing of this physical device. It does not involve any artificial intelligence, machine learning, or software-driven diagnostic capabilities that would require the kind of performance evaluation requested in the prompt.

    Therefore, I cannot extract the requested information regarding acceptance criteria, reported device performance, sample sizes for test/training sets, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, or ground truth establishment relevant to an AI/ML device.

    The information provided in the document is about the physical device itself.

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