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510(k) Data Aggregation

    K Number
    K992874
    Date Cleared
    1999-11-18

    (84 days)

    Product Code
    Regulation Number
    890.5525
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    Q-SWEAT QUANTITATIVE SWEAT MEASUREMENT SYSTEM

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

    The Q-Sweat™ Quantitative Sweat Measurement System is designed to measure the sweat output of the skin of humans. This device does not make a diagnosis or indicate by itself that any disease state exists; it simply documents sweat output. This device is to be used in scientific studies of the anatomy, physiology, and biochemistry of the skin & associated structures.

    Device Description

    The O-Sweat device is measurement-only device which is designed to measure the rate & volume of sweating by capturing a sample of sweat (water) inside a small measuring chamber which is affixed to the skin. It does not measure any other parameters of the sweat sample. The measurement made is simply a calculation of moisture given off by the skin.

    AI/ML Overview

    Here's an analysis of the provided text regarding the Q-Sweat Quantitative Sweat Measurement System, focusing on acceptance criteria and study details.

    Based on the provided text, there is no specific study described that proves the device meets explicitly stated acceptance criteria. The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed performance study results against predefined metrics.

    The document primarily states that the device is substantially equivalent to other sweat measuring devices. This is a claim made by the applicant, and the FDA's letter indicates their agreement with this claim for market clearance, not necessarily a detailed performance validation against specific criteria.

    Therefore, many of the requested sections (1, 2, 3, 4, 5, 6, 7, 8, 9) cannot be fully populated as the information is not present in the provided text.

    However, I can extract the relevant available information:


    1. Table of acceptance criteria and the reported device performance

    Acceptance Criteria (Explicitly stated)Reported Device Performance
    Not explicitly stated in terms of quantitative metrics or thresholds.The device operates in "nearly exact same fashion" as predicate devices. The use of the device does not affect the body differently or raise new questions of safety or effectiveness compared to predicate devices.

    2. Sample sized used for the test set and the data provenance

    • Sample Size (Test Set): Not specified in the provided text.
    • Data Provenance: Not specified in the provided text.

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

    • This information is not provided in the text as no specific performance study is detailed with an independent ground truth establishment.

    4. Adjudication method for the test set

    • This information is not provided in the text as no specific performance study is detailed.

    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.

    • No information about an MRMC study or AI assistance is present. This device is a measurement-only system and does not appear to involve AI or human interpretation in the context of diagnostic decision-making that would necessitate an MRMC study.

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

    • The document implies standalone performance comparison to predicate devices, but no dedicated "standalone study" with detailed metrics is described. The device is a direct measurement tool, not an algorithm.

    7. The type of ground truth used

    • This information is not provided in the text as no specific performance study is detailed with an independent ground truth. The basis of equivalence is implied to be through comparison with the operational principles and intended use of existing predicate devices.

    8. The sample size for the training set

    • Not applicable/Not provided. The text does not describe a machine learning model or a training set.

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

    • Not applicable/Not provided. The text does not describe a machine learning model or a training set.

    Summary of what is provided:

    The document is a 510(k) summary for the Q-Sweat Quantitative Sweat Measurement System. Its primary goal is to establish substantial equivalence to existing predicate devices.

    • Intended Use: "To measure the sweat output of the skin of humans. This device does not make a diagnosis or indicate by itself that any disease state exists; it simply documents sweat output. This device is to be used in scientific studies of the anatomy, physiology, and biochemistry of the skin & associated structures."
    • Comparison to Predicate Devices: The applicant claims the Q-Sweat operates in "nearly exact same fashion" as predicate devices and does not raise new questions of safety or effectiveness. The FDA's letter confirms substantial equivalence, allowing the device to be marketed.
    • Device Type: A "measurement-only device" designed to measure the rate and volume of sweating. It captures a sample of sweat inside a small measuring chamber affixed to the skin and calculates moisture given off by the skin.

    The absence of detailed performance study data, acceptance criteria, and ground truth establishment methods is typical for 510(k) applications that rely heavily on demonstrating substantial equivalence to already cleared devices, especially for devices where the technology and intended use are well-established. The equivalence claim focuses on the device's operational similarity and lack of new safety/effectiveness concerns, rather than a quantifiable performance comparison against specific metrics.

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