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510(k) Data Aggregation

    K Number
    K152166
    Date Cleared
    2016-07-11

    (342 days)

    Product Code
    Regulation Number
    880.5570
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    JSM insulin pen needle is intended for use with pen injector devices for the subcutaneous injection of insulin in the treatment of diabetes.

    Device Description

    JSM insulin pen needle consists of a cap, needle, hub and paper. The cap and paper function to maintain the sterility of the needle. This device is single use and sterilized by EO gas. The hub can be connected with pen injector. After removing the paper, the needle can be screwed on to the pen. The needle cap protects the needle. This device has various gauge sizes (29G, 30G, 31G, 32G) and length (4mm, 5mm, 6mm, 10mm, 12mm) The color of needle cap per needle gauge is distinguished according to the standard given by our company.

    AI/ML Overview

    The provided text describes a 510(k) premarket notification for a medical device, the JSM Insulin Pen Needle. It details the device's characteristics, comparison to a predicate device, and performance testing results against various ISO standards.

    However, the questions posed (regarding acceptance criteria and a study proving a device meets these criteria, especially concerning AI/human reader performance, ground truth, and training data) are not applicable to this document.

    This document describes the regulatory approval process for a physical medical device (an insulin pen needle) and focuses on engineering and biocompatibility performance data, not artificial intelligence or diagnostic algorithms. There is no mention of "AI" or "human readers" or "ground truth" in the context of an algorithmic assessment.

    Therefore, I cannot provide a table of acceptance criteria, device performance, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC studies, standalone algorithm performance, or ground truth establishment based on the provided text. These concepts are relevant to the evaluation of AI/ML-driven diagnostic devices, which is not what this document describes.

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