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510(k) Data Aggregation

    K Number
    K221854
    Device Name
    Reprocesses Umbilical Cable
    Manufacturer
    Innovative Health, LLC.
    Date Cleared
    2022-08-06

    (40 days)

    Product Code
    NLH
    Regulation Number
    870.1220
    Type
    Traditional
    Panel
    Cardiovascular
    Reference & Predicate Devices
    K211662,K193263
    Why did this record match?
    Device Name :

    Reprocesses Umbilical Cable

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

    The reprocessed cable provides an electrical connection between the IntellaMap Orion Mapping Catheter and the Signal Station of the Rhythmia Mapping System. The reprocessed Umbilical Cable is intended to be used with an Orion Mapping Catheter during electrophysiology procedures, electroanatomical mapping, intracardiac stimulation (pacing) and/or recording of electrical potentials.

    Device Description

    The reprocessed umbilical cable is an insulated, multi-conductor cable that us 200 cm (6.6 ft) in length with multi-pin connectors at each end.

    AI/ML Overview

    The provided document is a 510(k) summary for a Reprocessed Umbilical Cable, not an AI/medical imaging device. As such, it does not contain the information required to answer the prompt, which is specifically looking for details about the acceptance criteria and study proving performance for an AI device.

    The document describes the reprocessing of a physical medical cable and includes information on:

    • The device description and its intended use (providing an electrical connection between a mapping catheter and a signal station).
    • Bench and laboratory testing performed for safety and effectiveness, such as cleaning validation, sterilization validation, visual inspection, dimensional verification, electrical continuity testing, HiPOT testing, and packaging validation.
    • The reprocessed cable's substantial equivalence to a predicate device.

    It does not contain any information about:

    • Acceptance criteria related to AI performance metrics (e.g., sensitivity, specificity, AUC).
    • Sample sizes for test sets or training sets for an algorithm.
    • Data provenance for AI training/testing data.
    • Expert involvement in establishing ground truth for AI.
    • Adjudication methods for AI ground truth.
    • MRMC studies or human reader improvement with AI assistance.
    • Standalone algorithm performance.
    • Ground truth types like pathology or outcomes data for AI.

    Therefore, I cannot fulfill the request as the source document does not pertain to an AI device.

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