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510(k) Data Aggregation

    K Number
    K242798
    Device Name
    Airmod
    Date Cleared
    2025-02-28

    (165 days)

    Product Code
    Regulation Number
    868.2375
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    Airmod, when used in conjunction with Accursound Electronic Stethoscope AS-101, is a software as medical device intended to be used for the continuous, non-invasive monitoring of respiratory rate (RR) in adult patients who are subjected to procedural sedation and/or anesthesia.

    Airmod is intended for use by healthcare professionals in hospitals and healthcare facilities who are legally credentialed to perform procedural sedation and/or anesthesia.

    Airmod is intended for Android-based devices only.

    Device Description

    Airmod 114 is an Android-based software application designed to aid healthcare professionals by monitoring a sedated and/or anesthetized patient's breathing in real time. The device has an AIbased algorithm that can detect inhalation acoustics and provides respiratory rates based on the analysis of the acoustic signals of breathing sounds collected by AccurSound Electronic Stethoscope AS-101. Airmodid for the continuous monitoring of respiratory rate (RR) in adults who are subjected to procedural sedation. Airmod™ is designed for use in hospitals and healthcare facilities performing procedural sedation/anesthesia. The device is not intended for patients who are not anesthetized/sedated.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and study details for the Airmod device, based on the provided FDA 510(k) summary:

    Acceptance Criteria and Device Performance

    Acceptance CriteriaReported Device Performance (Airmod™)
    Root Mean Square Error (RMSE) < 3 Breaths Per Minute (BPM) when compared to manual-scored capnography (mancRR)RMSE = 2.689 BPM (95% CI: 2.529, 2.695)

    Study Details

    1. Sample Size Used for the Test Set and Data Provenance:
    * Sample Size: 270 participants
    * Data Provenance: The study included both "outside of US (OUS) and US populations." This indicates a combination of retrospective and prospective data, though the document does not explicitly state the temporal nature of the data collection for each region.

    2. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications:
    * The document mentions "manual scored capnography (mancRR)" as the reference for ground truth. However, it does not specify the number of experts used or their qualifications for establishing this ground truth.

    3. Adjudication Method:
    * The document does not specify the adjudication method used for establishing the ground truth from manual-scored capnography.

    4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
    * No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The clinical testing focused on the non-inferiority of Airmod™'s respiratory rate measurements compared to a capnography system. There is no mention of human readers improving with or without AI assistance.

    5. Standalone Performance Study:
    * Yes, a standalone performance study was done. The clinical testing evaluated the performance of Airmod™ (algorithm only, as a software as medical device) against the Capnostream™35 capnography system. The results of the RMSE directly reflect the algorithm's standalone accuracy.

    6. Type of Ground Truth Used:
    * The ground truth used was manual-scored capnography (mancRR). This provides a direct, physiological measurement of respiratory rate that is then manually interpreted or verified.

    7. Sample Size for the Training Set:
    * The document does not specify the sample size used for the training set of the AI-based algorithm. The 270 participants relate to the clinical testing (test set) for validation.

    8. How the Ground Truth for the Training Set Was Established:
    * The document does not provide details on how the ground truth for the training set was established. It only mentions that the device has an "AI-based algorithm that can detect inhalation acoustics and provides respiratory rates based on the analysis of the acoustic signals."

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