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510(k) Data Aggregation

    K Number
    K142051
    Date Cleared
    2014-12-17

    (142 days)

    Product Code
    Regulation Number
    870.1330
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    PRELUDE PLASTIC JACKETED GUIDE WIRE

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

    The Merit plastic jacketed introducer guide wire is intended to facilitate the placement of devices during diagnostic and interventional procedures, specifically sheath introducers. The wire is indicated for the peripheral vasculature only.

    Device Description

    The Merit Prelude Plastic jacketed introducer guide wire consists of a high quality metallic core wire with a radiopaque polymer jacket. The wire will be offered in straight and angled versions, in various lengths.

    AI/ML Overview

    The provided document is a 510(k) Summary for a medical device (Prelude Plastic Jacketed Guide Wire). It describes the device, its intended use, comparison to a predicate device, and a summary of safety and performance tests. However, it does not contain a detailed table of acceptance criteria and reported device performance with specific values. Instead, it broadly states that tests were conducted according to standards and met acceptance criteria.

    Therefore, many of the requested elements cannot be fully extracted or are explicitly stated as not applicable to this type of submission.

    Here's an analysis based on the information available:


    1. A table of acceptance criteria and the reported device performance

    The document does not provide a specific table with quantitative acceptance criteria and corresponding reported device performance values. It generally states that "A battery of testing was conducted in accordance with protocols based on requirements outlined in guidance's and industry standards and these were shown to meet the acceptance criteria that were determined to demonstrate substantial equivalence."

    It lists the types of tests performed in the "Safety & Performance Tests" section, but without specific criteria or results.

    2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    The document does not specify the sample sizes used for the test set or the data provenance. This level of detail is typically found in the full test reports, not usually in the 510(k) summary.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

    Not applicable to this type of device (guide wire). The "tests" are primarily physical, chemical, and biological evaluations, not diagnostic assessments requiring expert interpretation of images or patient data.

    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

    Not applicable. See point 3.

    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 device is a medical instrument (guide wire), not an AI/software device that assists human readers.

    6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done

    Not applicable. This is not an algorithm or AI device.

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

    For the performance tests listed (e.g., tensile strength, flexibility, biocompatibility), the "ground truth" would be established by the defined test methodologies and expected physical/chemical properties as outlined in the referenced standards (e.g., ISO, ASTM, USP). For example, a "fracture test" would have an acceptance criterion that the wire should not fracture under certain conditions, and the "ground truth" would be the observation of fracture or no fracture.

    8. The sample size for the training set

    Not applicable. This type of device does not involve a "training set" in the context of machine learning or AI.

    9. How the ground truth for the training set was established

    Not applicable. See point 8.

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