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510(k) Data Aggregation

    K Number
    K955604
    Date Cleared
    1996-04-19

    (133 days)

    Product Code
    Regulation Number
    866.5100
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    RELISA SM/RNP ANTIBODY TEST SYSTEM

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

    This test system is for in vitro diagnostic use for the detection of antibodies to nuclear antigens Sm (Smith) and RNP (U1-RNP or ribonucleoprotein) in human serum.

    Device Description

    This is an enzyme immunoassay for the detection of antibodies to nuclear antigens Sm (Smith) and RNP (U1-RNP or ribonucleoprotein) in human serum.

    AI/ML Overview

    The provided text describes a RELISA® Sm/RNP Antibody Test System which is an enzyme immunoassay for the detection of antibodies to nuclear antigens Sm (Smith) and RNP (U1-RNP or ribonucleoprotein) in human serum.

    Here's an analysis of the acceptance criteria and the study proving the device meets them:

    1. Table of Acceptance Criteria and Reported Device Performance

    The acceptance criteria are not explicitly stated as numerical thresholds in the provided text. Instead, the study aims to demonstrate substantial equivalence to a predicate device (RELISA® ENA Antibody Screening Tests System, K935129) by showing high relative sensitivity, relative specificity, and overall agreement. Based on the reported results, it appears the acceptance criteria were implicitly 100% for all these metrics compared to the predicate.

    MetricAcceptance Criteria (Implicit)Reported Device Performance (Sm Autoantigen)Reported Device Performance (RNP Autoantigen)
    Relative Sensitivity100%100.0%100.0%
    Relative Specificity100%100.0%100.0%
    Overall Agreement100%100.0%100.0%

    2. Sample Size Used for the Test Set and Data Provenance

    • Sample Size (Sm Autoantigen):

      • Positive: 32
      • Borderline: 4
      • Negative: 102
      • Total: 138 samples
    • Sample Size (RNP Autoantigen):

      • Positive: 40
      • Borderline: 5
      • Negative: 93
      • Total: 138 samples
    • Data Provenance: Not explicitly stated in the provided text (e.g., country of origin, retrospective or prospective). It is simply referred to as "human serum" used in direct comparison with the predicate device.

    3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

    Not applicable. The "ground truth" for this study was established using a predicate device, not human experts.

    4. Adjudication Method for the Test Set

    Not applicable. There was no human adjudication as the comparison was made against a predicate device.

    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 study does not involve AI assistance or human readers in the context of an MRMC study. It's a direct comparison of a new immunoassay device against an existing immunoassay device.

    6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

    Yes, in a sense. The study evaluated the standalone performance of the new device (Immuno Concepts RELISA® Sm/RNP) by directly comparing its results to the standalone performance of the predicate device (Immuno Concepts RELISA® Screening Assay). There is no human interpretion involved in the comparison, only the output of the immunoassay devices.

    7. The Type of Ground Truth Used

    The ground truth used was the results obtained from a legally marketed predicate device (Immuno Concepts RELISA® Screening Assay, K935129). This is a form of reference method comparison, where the established performance of the predicate serves as the gold standard for evaluating the new device.

    8. The Sample Size for the Training Set

    Not applicable. This type of immunoassay device development and validation typically does not involve a "training set" in the machine learning sense. The device is chemical/biological in nature, and its parameters are established through laboratory optimization and validation, not through learning from a labeled dataset.

    9. How the Ground Truth for the Training Set Was Established

    Not applicable, as there is no "training set" in the context of this device and study design.

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