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510(k) Data Aggregation

    K Number
    K121912
    Device Name
    E1 EAR SENSORS
    Manufacturer
    Date Cleared
    2012-07-24

    (22 days)

    Product Code
    Regulation Number
    870.2700
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    E1 EAR SENSORS

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

    The Masimo E1 Ear Sensor is indicated for single patient use for continuous noninvasive monitoring of functional oxygen saturation of arterial hemoglobin (SpO2) and pulse rate (measured by an SpO2 sensor) for use with adult and pediatric patients, (weighing > 30kg), who are well or poorly perfused, in hospitals, hospital-type facilities, mobile, and home environments.

    Device Description

    The Masimo El Ear Sensor is a single patient use device that is designed to be used with instruments that include or are compatible with the Masimo SET and Masimo Rainbow SET technologies. The subject device is a modification of the existing device that provides improved oxygen saturation measurement accuracy.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and the study information for the Masimo E1 Ear Sensor, based on the provided text:

    Acceptance Criteria and Device Performance

    Accuracy (ARMS)/ConditionsAcceptance Criteria (Subject Device)Reported Device Performance (Subject Device)
    No motion/Well-perfusedSpO2: ± 2.5%SpO2: ± 2.5%
    No motion/Poorly-perfusedSpO2: ± 2.5%SpO2: ± 2.5%
    Pulse Rate (all conditions)3 beats/minute3 beats/minute

    Note: The reported device performance matches the acceptance criteria, indicating the device meets the stated accuracy specifications.

    Study Information

    2. Sample size used for the test set and the data provenance:

    • Test Set Sample Size: Not explicitly stated in the provided text. The document mentions "clinical validation studies" were performed but does not break down the number of subjects or data points used in these studies.
    • Data Provenance: Not explicitly stated in the provided text. The document does not specify the country of origin of the data or whether the studies were retrospective or prospective.

    3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

    • The document does not provide details on the number or qualifications of experts used to establish ground truth.

    4. Adjudication method for the test set:

    • The document does not specify any adjudication method used for the test set.

    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 device is a pulse oximeter sensor, not an AI-assisted diagnostic tool. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not applicable and was not performed.

    6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done:

    • Yes, a standalone performance assessment was done for the device. The "Accuracy (ARMS)" values are a measure of the device's accuracy in measuring oxygen saturation and pulse rate directly, without human interpretation or intervention in the measurement process itself. The clinical validation studies assessed the device's ability to meet its specified accuracy.

    7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

    • For an SpO2 device, the ground truth for oxygen saturation is typically established through arterial blood gas analysis (co-oximetry), which is considered the gold standard. While not explicitly stated in the provided text, this is the standard method for validating pulse oximeter accuracy.

    8. The sample size for the training set:

    • This document describes a medical device, specifically a pulse oximeter sensor, and its accuracy. The concept of a "training set" typically applies to machine learning or AI models. This device does not appear to involve a machine learning algorithm that requires a separate training set as understood in AI development. The device's performance is based on its hardware and internal algorithms for signal processing.

    9. How the ground truth for the training set was established:

    • Not applicable, as noted in point 8.
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