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510(k) Data Aggregation

    K Number
    K201843
    Date Cleared
    2020-10-23

    (109 days)

    Product Code
    Regulation Number
    880.2910
    Reference & Predicate Devices
    N/A
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Infrared ear and forehead thermometer model YI-100 is a multi-function infrared thermometer intended for the intermittent measurement of human body temperature by Ear mode, or by Forehead mode (moving the mead from one temple to another temple for forehead measurement). The device can be used on people of one month old and above.

    Device Description

    Infrared ear and forehead thermometer, model: YI-100

    AI/ML Overview

    The provided text is a 510(k) clearance letter from the FDA for an infrared ear and forehead thermometer (model YI-100). It focuses on regulatory approval and substantial equivalence to predicate devices, rather than detailed performance study results with specific acceptance criteria, sample sizes, expert qualifications, or ground truth methodologies.

    Therefore, many of the requested details about acceptance criteria and the study proving the device meets them are not present in this document. The document confirms that the device has been reviewed and cleared based on substantial equivalence, implying that its performance is considered acceptable relative to legally marketed predicate devices.

    Here's what can be extracted and what cannot:

    1. A table of acceptance criteria and the reported device performance:
    This information is not available in the provided document. The FDA letter is a regulatory clearance, not a performance report. Performance data and acceptance criteria would be part of the predicate device's documentation or the manufacturer's submission which is not included here.

    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 available in the provided document.

    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 available in the provided document. For a thermometer, "ground truth" would typically involve comparison to a highly accurate reference thermometer, rather than expert interpretation.

    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set:
    This information is not available in the provided document. Adjudication methods are usually relevant for subjective interpretations of medical images, not for objective measurements like temperature.

    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:
    This information is not available in the provided document. An MRMC study is not relevant for a thermometer, which is a standalone measurement device, not an AI-assisted diagnostic tool for human readers.

    6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
    For a thermometer, the device itself is the standalone performance. The document implies that the device's performance was evaluated against standards for clinical electronic thermometers (21 CFR 880.2910), but specific study details are not provided.

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc):
    For a thermometer, the "ground truth" typically involves comparison to a highly accurate, calibrated reference thermometer (e.g., a mercury-in-glass thermometer or a highly precise electronic lab thermometer). This specific detail is not explicitly stated in the provided document, but it's the standard practice for validating thermometer accuracy.

    8. The sample size for the training set:
    This information is not available in the provided document. This device is an infrared thermometer, not a machine learning or AI device that would typically have a "training set" in the context of diagnostic algorithms.

    9. How the ground truth for the training set was established:
    This information is not available and not applicable, as this device does not utilize a "training set" in the context of an AI/ML algorithm.

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