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510(k) Data Aggregation

    K Number
    K073585
    Date Cleared
    2008-04-17

    (119 days)

    Product Code
    Regulation Number
    870.1200
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    INNERCHANGE MICRO-INTRODUCER CATHETER, MODELS 7900, 7901, 7902, 7903, 7904 AND 7905

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

    The INNERCHANGE micro-introducer catheter is intended for use in accessing the vascular system through a small gauge needle stick and for delivering radiopaque media to selected sites in the vascular system in conjunction with routine diagnostic procedures.

    Device Description

    The INNERCHANGE micro-introducer catheter combines a micro-introducer kit for obtaining vascular access with a hydrophilically coated diagnostic catheter. The INNERCHANGE catheter is designed to be used for delivering radiopaque media to selected sites in the vascular system in conjunction with routine diagnostic procedures. The INNERCHANGE micro-introducer catheter is compatible with ≤0.038" / 965mm guidewires. Each INNERCHANGE micro-introducer catheter consists of the following components:

    • . 21G percutaneous entry needle
    • 0.018" guidewire .
    • Dilator .
    • Catheter with selected tip shape and attached stopcock .
    AI/ML Overview

    The provided document is a 510(k) summary for the InnerChange Micro-Introducer Catheter. It describes the device, its intended use, a summary of non-clinical testing, and concludes with a statement of equivalence to a predicate device.

    Crucially, this document does not describe the acceptance criteria or a study that proves the device meets specific performance criteria in the way typically associated with AI/ML-driven devices (e.g., sensitivity, specificity, AUC). Instead, it describes a medical device that undergoes engineering and biocompatibility testing.

    Therefore, many of the requested fields are not applicable to the information contained in this submission. I will answer based on the information provided, explicitly stating when information is not present or not applicable.


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

    Acceptance Criteria (Bench Test)Reported Device Performance
    RadiopacityConfirmed suitability
    Visual inspection (lubricious coating)Confirmed suitability
    Visual inspection of catheter/dilatorConfirmed suitability
    Catheter Curve retentionConfirmed suitability
    Dilator passageConfirmed suitability
    Dilator/catheter tapered tip assemblyConfirmed suitability
    Guidewire passageConfirmed suitability
    Catheter Tortuosity testConfirmed suitability
    Hydrophilic coating inspection after tortuosityConfirmed suitability
    Flow rate with contrastConfirmed suitability
    Dynamic pressure test (catheter only)Confirmed suitability
    Kink radiusConfirmed suitability
    Liquid leak test (catheter only)Confirmed suitability
    Proximal to distal shaft strengthConfirmed suitability
    Biocompatibility assessmentsConfirmed suitability
    Shelf life testingConfirmed suitability

    Note: The document states "The results of this testing confirmed the suitability of the InnerChange Micro-Introducer Catheter for its intended use." It does not provide specific quantitative acceptance criteria or raw performance data for each test, only that the device "confirmed suitability."

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

    This information is not provided in the document. The document refers to "testing conducted" but does not detail sample sizes for each bench test or data provenance. There is no mention of a "test set" in the context of clinical data, as no clinical testing was performed.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

    This information is not applicable/not provided. The device is a physical medical instrument, not an AI/ML diagnostic tool requiring expert-established ground truth for a test set. The evaluation was based on engineering bench tests and biocompatibility, not interpretations by human experts.

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

    This information is not applicable/not provided. As no clinical or expert-reviewed "test set" was used, no adjudication method was necessary or mentioned.

    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

    No, a multi-reader, multi-case comparative effectiveness study was not done. This document is for a physical medical catheter, not an AI/ML system, so this type of study is irrelevant to this submission.

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

    This is not applicable. The device is a physical catheter, not an algorithm.

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

    This is not applicable. For this type of medical device, the "ground truth" for the bench tests would be defined specifications, engineering standards, and biological compatibility requirements rather than clinical ground truth like pathology or outcomes data. The document states "The results of this testing confirmed the suitability of the InnerChange Micro-Introducer Catheter for its intended use," implying compliance with predefined engineering and safety standards.

    8. The sample size for the training set

    This information is not applicable/not provided. The device is a physical product. There is no concept of a "training set" as understood in machine learning. Design verification involved bench testing, not machine learning model training.

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

    This information is not applicable/not provided. As there is no training set for an AI/ML model, the concept of establishing ground truth for it does not apply.

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