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510(k) Data Aggregation

    K Number
    K103316
    Device Name
    FLOWREST
    Manufacturer
    Date Cleared
    2011-02-08

    (90 days)

    Product Code
    Regulation Number
    868.5450
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    FLOWREST

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

    The Flowrest® delivers warmed humidified high-flow breathing gases. The Flowrest® is intended for treating spontaneously breathing patients who require warmed and humidified high-flow respiratory gases within the homecare, subacute and hospital settings.

    Device Description

    The Flowrest® is an integrated non-invasive high flow device incorporating a blower, an air flow heater, a heated humidifier, and a dual lumen breathing circuit and cannula. Filtered room air is drawn in through a blower, where it passes a heater. The heated air is split between the inner lumen and outer lumen of the dual lumen breathing circuit. The heated air in the inner lumen passes through a heated humidifier and is delivered to the patient through a cannula. The heated air in the outer lumen passes through the entire length of the breathing circuit and serves to insulate the humidified patient air, thus minimizing condensation and rainout.

    The Flowrest® is comprised of two functional, yet integrated components. One is a motorized fan assembly that provides the physician prescribed high flows of heated breathing and insulating gases between 15 and 35 liters per minute. The fan speed is directly related to flow rate and is controlled by software. The blower assembly output connects directly to a humidification chamber at the front of the device.

    The second component of the Flowrest® is a heated humidifier. The water is contained in the humidification chamber positioned on a heater plate at the front of the unit. The chamber connects directly to the blower assembly. Air flow from the device passes through the heated humidification chamber, is warmed and humidified and after passing through the breathing circuit, is delivered to the patient through a cannula. The second flow of warm air through the outside lumen of the delivery tube bypasses the humidification chamber through the top of the humidifier chamber. Ambient temperature is monitored in order to reduce humidified patient air condensation. Temperature controls are controlled by software.

    AI/ML Overview

    The provided document is a 510(k) summary for the Vapotherm Flowrest®, a respiratory gas humidifier. It details the device's intended use, technological characteristics, and a comparison to a predicate device to establish substantial equivalence.

    However, the document does not contain the kind of information typically found in studies proving an AI/Machine Learning device's performance against acceptance criteria. Specifically, it lacks:

    • A table of acceptance criteria for AI performance metrics (e.g., sensitivity, specificity, AUC).
    • Details about a test set (sample size, provenance).
    • Information on expert ground truth establishment (number of experts, qualifications, adjudication).
    • MRMC studies, standalone AI performance, or training set details.
    • The type of ground truth used (pathology, outcomes data, etc.)

    The document focuses on the engineering and safety performance of a medical device (a humidifier) against established medical device standards (e.g., ISO, IEC), and its substantial equivalence to a predicate device.

    Therefore, I cannot fulfill your request for AI-specific performance criteria and study details based on the provided text. The "Performance Data" section in the document refers to compliance with safety and design standards for a gas humidifier, not the performance of an AI algorithm.

    If this was an AI/ML device, the information would typically look like this (hypothetically, what you are asking for):

    1. A table of acceptance criteria and the reported device performance (Hypothetical for an AI device):

    MetricAcceptance CriteriaReported Device Performance (Hypothetical)
    Sensitivity≥ 90% for detecting Condition X92.5%
    Specificity≥ 80% for ruling out Condition X85.1%
    Area Under ROC Curve≥ 0.900.93
    Inference Time
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