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510(k) Data Aggregation

    K Number
    K053330
    Date Cleared
    2005-12-16

    (15 days)

    Product Code
    Regulation Number
    862.1150
    Reference & Predicate Devices
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Abbott ARCHITECT B12 Calibrators are for the calibration of the ARCHITECT i System when used for the quantitative determination of vitamin B12 in human serum and plasma.

    Device Description

    The Abbott ARCHITECT B12 Calibrators are liquid, ready-for-use materials in a buffered aqueous solution. Concentrations of the calibrator components span the dynamic range of the assay.

    AI/ML Overview

    The provided text describes the Abbott ARCHITECT® B12 Calibrators, which are for calibrating the ARCHITECT i System for quantitative determination of vitamin B12 in human serum and plasma. The study presented is a correlation analysis between the new 6-Point Calibrators and the predicate 2-Point Calibrators.

    Here's a breakdown of the requested information based on the provided text:

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

    The acceptance criteria are not explicitly stated in the provided text. However, the study aims to demonstrate that the performance of the new 6-Point Calibrators is "comparable to the performance of the predicate device." The reported performance metrics are based on a correlation analysis.

    MetricAcceptance Criteria (Implied)Reported Device Performance (6-Point vs. 2-Point Calibrators)
    Correlation (r)High correlation (e.g., close to 1)0.998
    Slope (Least Squares)Close to 10.96
    Intercept (Least Squares)Close to 022
    Slope (Passing-Bablok)Close to 10.98
    Intercept (Passing-Bablok)Close to 012

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

    • Sample size used for the test set: 495 specimens
    • Data provenance: Not explicitly stated, but the specimens were "serum specimens tested." The country of origin and whether the data was retrospective or prospective are not mentioned.

    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. The study is a comparison of two calibration methods, not a diagnostic test requiring expert ground truth in the traditional sense. The "ground truth" here is the measurement obtained by the predicate device's 2-Point Calibrators.

    4. Adjudication method for the test set

    This information is not applicable as it's a direct comparison of calibration methods and not a study requiring adjudication of diagnostic interpretations by experts.

    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, a multi-reader multi-case (MRMC) comparative effectiveness study was not done. This device is a calibrator for an automated assay, not an AI-assisted diagnostic tool that would involve human readers.

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

    This concept is not entirely applicable in the context of a calibrator for an automated immunoassay. The device is a component that enables standalone (algorithm-only, or in this case, instrument-only) performance of the ARCHITECT i System for B12 measurement. The study directly assesses the performance of this calibrator in conjunction with the instrument.

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

    The "ground truth" for this study is essentially the results obtained using the predicate device's 2-point calibration assay. The new 6-point calibrators are being evaluated against the established performance of the existing calibrators.

    8. The sample size for the training set

    This information is not provided and is likely not relevant for this type of calibrator evaluation. Calibrators are developed based on specific chemical and manufacturing processes, not typically "trained" on large datasets in the way an AI algorithm would be.

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

    This information is not provided and is likely not applicable for the reasons stated in point 8.

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