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510(k) Data Aggregation

    K Number
    K032563
    Date Cleared
    2003-12-23

    (125 days)

    Product Code
    Regulation Number
    862.1155
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    CLINITEST HCG PREGNANCY TEST

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

    The Bayer Healthcare Clinitest hCG Pregnancy Test is for in vitro diagnostic use as a qualitative method in the rapid detection of human chorionic gonadotropin (hCG) in urine specimens. The test is utilized with the Clinitest Status analyzer and is intended for near patient (point of care) and centralized laboratory locations.

    Device Description

    The Clinitest® hCG is a qualitative test for the rapid detection of human chorionic gonadotropin (hCG) in urine. The device is read by the Clinitek Status instrument.

    AI/ML Overview

    This document describes a 510(k) premarket notification for the Clinitest® hCG Pregnancy Test. The information provided focuses on the device's substantial equivalence to predicate devices and general regulatory information, rather than a detailed study proving the device meets specific acceptance criteria with performance metrics.

    Therefore, many of the requested sections (e.g., sample sizes for test and training sets, expert qualifications, adjudication methods, MRMC study details, ground truth specifics for training) cannot be extracted from the provided text. The document is a regulatory submission summary, not a full study report.

    However, I can extract the following based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    The document does not explicitly state numerical "acceptance criteria" for performance metrics (like sensitivity, specificity, or accuracy) or report specific device performance values in a table. It only states that the device has "similar technological characteristics, device performance and intended use" to its predicate devices.

    Acceptance CriteriaReported Device Performance
    Not explicitly stated as numerical criteria in the provided text. The overarching "acceptance criterion" from a regulatory perspective is "substantial equivalence" to predicate devices.The document states the device has "similar technological characteristics, device performance and intended use" as the predicate devices. It provides no specific numerical performance data (e.g., sensitivity, specificity, accuracy).

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

    Not provided in the document.

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

    Not provided in the document.

    4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

    Not provided in the document.

    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

    Not applicable. This device is a rapid diagnostic test read by an instrument (Clinitek Status analyzer), not an AI-assisted diagnostic imaging device requiring human reader interpretation in the context of MRMC studies.

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

    The device, when read by the Clinitek Status instrument, operates in a "standalone" manner for interpreting the test result (positive, borderline, or negative). However, the document does not provide a standalone performance study report with specific metrics. It simply describes the device's function.

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

    Not provided in the document. For pregnancy tests, ground truth typically involves confirmation with a more definitive method (e.g., quantitative hCG measurement, clinical outcome).

    8. The sample size for the training set

    Not applicable/Not provided. This is a point-of-care rapid diagnostic test, not an AI/machine learning device that would typically have a "training set" in the computational sense. The "training" for such devices usually refers to manufacturing process development, reagent optimization, and internal verification/validation, not a dataset for algorithm training.

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

    Not applicable/Not provided (as per point 8).

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