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510(k) Data Aggregation

    K Number
    K991623
    Date Cleared
    1999-08-09

    (90 days)

    Product Code
    Regulation Number
    876.5540
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K955182

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

    The AVF sets are intended to be used for intravenous injection and connection with dialysis blood lines to transport blood from the artery of the patient to the dialyzer and transport blood after dialysis to the vein of the patient.

    Device Description

    Arterial Venous Fistula Needles are described in blood access device and accessories (nonimplanted) 21 CFR 876.5540 The Disposable Arterial Venous Fistula Needles that we intend to market include 1 type of design: fixed wing. It is offered with or without a clamp. The device consists of a winged needle (14 - 17 gauge) connected to polyvinylchoride AVF tubing which is connected to a AVF luer connector. Needles with a hole (back eye type) and without a hole are available as well and are both 1 inch and 1¼ inches in length. Tubing is available in lengths of 300 mm. The materials used for the components include: polyvinylchloride (PVC), stainless steel (SS), polyoxymethylene (POM), polycarbonate (PC) and polypropylene (PP).

    AI/ML Overview

    The provided text is a 510(k) summary for the Nipro Arterial Venous Fistula Needle. This type of document is for a medical device cleared by the FDA through the substantial equivalence pathway, not an AI/ML powered device. Therefore, the requested information about acceptance criteria, study details, and AI/ML specific metrics (like sample sizes for test/training sets, expert ground truth, MRMC studies, standalone performance, etc.) is not applicable and will not be found in this document.

    The document describes the device, its intended use, and states that it is substantially equivalent to a legally marketed predicate device (K955182). Substantial equivalence means that the device is as safe and effective as the predicate device.

    To answer your request, I must state that the document does not contain the information you are looking for regarding acceptance criteria and studies in the context of AI/ML devices.

    Here's a breakdown of why each specific point you asked for cannot be answered from this text:

    1. A table of acceptance criteria and the reported device performance: This document asserts substantial equivalence based on technical characteristics being "identical" to a predicate device. It does not set forth specific performance acceptance criteria or report performance data.
    2. Sample sized used for the test set and the data provenance: Not applicable. This is a medical device, not an AI algorithm evaluated with a test set.
    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for an AI algorithm is not relevant here.
    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
    5. 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: Not applicable. This is a physical medical device, not an AI assistance tool for human readers.
    6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done: Not applicable.
    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable.
    8. The sample size for the training set: Not applicable.
    9. How the ground truth for the training set was established: Not applicable.

    In summary, the provided text describes a traditional medical device and its 510(k) clearance, which is a regulatory pathway demonstrating equivalence to another marketed device. It does not involve the type of performance studies or validation processes used for AI/ML based devices, which would include the metrics you listed.

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