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510(k) Data Aggregation

    K Number
    K222242
    Device Name
    FloPatch FP120
    Manufacturer
    Date Cleared
    2022-12-09

    (136 days)

    Product Code
    Regulation Number
    870.2100
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The FloPatch FP120 is indicated for use for the noninvasive assessment of blood flow in the carotid artery. FloPatch FP120 operates in a single mode, the Continuous Wave (CW) mode, and is not capable of operating in any other mode.

    The device is intended to be used by medical professionals, such as physicians and nurses, in hospitals and professional environments. The device is intended for prescription use on adults only.

    Device Description

    The FloPatch (FP120) is a non-invasive blood flow detection device to be used in a medical/hospital setting for use by a medical professional. The device uses ultrasound and the Doppler effect to assess the flow of blood. The device consists of a signal processing unit and an adhesive strap. The device transmits ultrasonic waves from the ultrasonic transducer to a peripheral vessel such as the carotid artery. The Doppler shifted ultrasonic waves are reflected by moving blood cells back to the ultrasonic flow transducer. The reflected signal is received by the signal processing unit which outputs the Doppler signal wirelessly to a mobile medical application. The mobile medical application then processes the Doppler signal and displays a Max Velocity trace, Max VTI (Velocity Time Integral) and the Corrected Flow Time.

    AI/ML Overview

    Here's an analysis of the provided text regarding the acceptance criteria and supporting studies for the FloPatch FP120 device, structured to answer your specific questions:

    1. Table of Acceptance Criteria and Reported Device Performance

    The submission describes product modifications to an already cleared device and demonstrates substantial equivalence through various tests. The acceptance criteria, while not explicitly stated as numerical values for all tests, are implied by a "Pass" result for each test performed.

    Acceptance Criteria (Implied)Reported Device Performance
    Device meets Class B EMC requirements (CISPR B).Pass
    Adhesive meets biocompatibility requirements.Pass
    Adhesive satisfies material and shelf life performance testing.Pass
    Packaging meets ISTA3 and performance testing.Pass
    Device meets applied part BF electrical safety requirements.Pass
    Software application satisfies functional requirements and performs as intended; algorithms and calculations were verified.Pass
    Device meets IP6X dust ingress requirements.Pass
    Device meets transport medical equipment requirements (shock, vibration, free fall).Pass
    Global maximum derated ISPTA ≤ 720 mW/cm² and global maximum MI ≤ 1.9 (Acoustic Output).Pass
    Device meets IEC 60601-2-37 for ultrasound safety.Pass

    2. Sample Size Used for the Test Set and Data Provenance

    The document does not provide specific sample sizes for the test sets for the various engineering validation and verification tests. The data provenance (e.g., country of origin, retrospective/prospective) is also not specified in this summary. These tests appear to be laboratory-based engineering and software validation tests rather than clinical studies with patient data.

    3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

    This information is not provided in the document. Given that the tests are primarily engineering and software verification, the "ground truth" would likely be established by technical specifications and standards rather than expert clinical consensus.

    4. Adjudication Method for the Test Set

    This information is not provided. For engineering and software tests, adjudication usually involves technical review against predefined specifications rather than a multi-expert clinical review process.

    5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

    No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance was not mentioned or described in this document. The device, FloPatch FP120, is a cardiovascular blood flowmeter that directly assesses blood flow and does not appear to involve human interpretation of images or data that would typically necessitate such a study.

    6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

    The "Software Testing" mentioned includes verification that "Algorithms and calculations were also verified." This implies that the algorithms were tested for their accuracy in isolation (standalone), but the details of this testing (e.g., specific metrics, dataset) are not provided. The device itself is user-operated by medical professionals, so an entirely "standalone" clinical performance might not be applicable in the same way it would be for an AI-powered diagnostic image analysis tool.

    7. The Type of Ground Truth Used

    For the engineering tests (EMC, Biocompatibility, Material/Shelf Life, Packaging, Electrical Safety, Dust Ingress, Emergency Services Equipment, Acoustic Output, Ultrasound Safety), the ground truth is based on regulatory standards and recognized industry specifications (e.g., Class B requirements, ISTA3, applied part BF requirements, IP6X, IEC 60601-1, IEC 60601-2-37, global maximum derated ISPTA, global maximum MI limits).

    For "Software Testing," the ground truth for "Algorithms and calculations" would be based on mathematical correctness and predefined functional requirements of the device's calculations (Max Velocity trace, Max VTI, Corrected Flow Time).

    8. The Sample Size for the Training Set

    The document does not mention any training sets. This submission is for modifications to a previously cleared device, not for the development of a new AI model that typically requires a training set. The "Software Testing" focuses on verification of existing algorithms and calculations rather than training a new machine learning model.

    9. How the Ground Truth for the Training Set Was Established

    As no training set is mentioned, this information is not applicable.

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