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510(k) Data Aggregation

    K Number
    K022885
    Device Name
    LINICAL PROTEIN 4 CALIBRATION VERIFIERS LEVELS-A-E FOR BECKMAN COULTER IMMAGE
    Manufacturer
    CLINIQA CORPORATION
    Date Cleared
    2002-10-02

    (33 days)

    Product Code
    JJY
    Regulation Number
    862.1660
    Type
    Traditional
    Panel
    Clinical Chemistry
    Reference & Predicate Devices
    N/A
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    LiniCAL™ Protein 4 Calibration Verifiers Levels A – E are intended for use as an assayed quality control material to verify calibration and/or assess linearity of the Beckman-Coulter Immage™

    Device Description

    LiniCAL™ Protein 4 Calibration Verifiers Levels A-E for Beckman Coulter Immage™

    AI/ML Overview

    The provided documents (K022885) are a 510(k) summary for the LiniCAL™ Protein 4 Calibration Verifiers Levels A-E for Beckman Coulter Immage™. This device is a quality control material and is classified as Class I. The FDA's letter states that the device is substantially equivalent to legally marketed predicate devices.

    Key takeaway: This submission pertains to a calibration verifier (quality control material) for an immunoassay system, not an AI/ML powered device. Therefore, many of the requested sections related to AI/ML device performance (like test sets, ground truth, MRMC studies) are not applicable to this type of medical device. The information provided focuses on demonstrating substantial equivalence to a predicate device, which is typical for Class I devices.

    Below is the information that can be extracted or deduced from the provided documents for this specific device:

    1. A table of acceptance criteria and the reported device performance

    The documents do not explicitly state quantitative acceptance criteria or detailed device performance metrics in a table format. For a calibration verifier, acceptance criteria would typically relate to the accuracy and precision of the assigned values for the control material and its ability to demonstrate linearity across its stated range when run on the target immunoassay system (Beckman-Coulter Immage™). The "reported device performance" for this type of device would generally involve demonstrating that the assigned values are stable, accurate, and that the material behaves as expected on the intended instrument. The FDA's substantial equivalence determination implies that these performance aspects were adequately addressed as compared to a predicate device.

    Acceptance CriteriaReported Device Performance
    Not explicitly stated in the provided documents, but would typically include:Not explicitly detailed in the provided documents, but would have been presented to the FDA to demonstrate substantial equivalence, likely including:
    - Accuracy of assigned values for each level (A-E)- Values assigned for each level on the Beckman Coulter Immage™ system.
    - Precision (lot-to-lot consistency, within-run, between-run precision)- Data supporting the stability and consistency of the assigned values.
    - Linearity demonstration over the specified range- Data demonstrating the material's ability to assess linearity on the target instrument.
    - Stability of the product- Stability study results (e.g., shelf-life, open-vial stability).
    - Compatibility with Beckman-Coulter Immage™- Data showing proper function on the Beckman-Coulter Immage™ system.

    As this is not an AI/ML device, the following points are generally not applicable or not detailed in this type of submission.

    2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    • Not applicable for this type of quality control material submission. Performance evaluation would involve running the controls on instruments, not analyzing patient data.

    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)

    • Not applicable. Ground truth for a calibrator verifier refers to its assigned values, which are determined through a comprehensive analytical process (e.g., reference methods, statistical analysis of multiple runs), not expert consensus on diagnostic images.

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

    • Not applicable. Adjudication methods are relevant for interpretation of complex data, typically for diagnostic devices.

    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 is not an AI-assisted diagnostic device requiring human reader studies.

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

    • Not applicable. This is not an algorithm-based device.

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

    • The "ground truth" for a calibration verifier is its assigned value, which is established through rigorous analytical testing using reference methods and comprehensive statistical analysis rather than expert consensus, pathology, or outcomes data. This process ensures accuracy and traceability.

    8. The sample size for the training set

    • Not applicable. This is not an AI/ML device that requires a "training set."

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

    • Not applicable. This is not an AI/ML device.
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