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510(k) Data Aggregation

    K Number
    K980180
    Manufacturer
    Date Cleared
    1998-02-12

    (23 days)

    Product Code
    Regulation Number
    862.2750
    Reference & Predicate Devices
    N/A
    Why did this record match?
    Device Name :

    2700 SAMPLE MANAGER

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

    The Waters 2700 Sample Manager is designed for use in a clinical laboratory to manage sample injection for high throughput LC and LCMS applications. The 2700 Sample Manager automates sample aspiration and injection from microtiter plates, test tubes, Eppendorf tubes, or conventional autosampler vials. This product is-intended for In Vitro Diagnostic Use.

    Device Description

    The Waters 2700 Sample Manager is designed for use in a clinical laboratory to manage sample injection for high throughput LC and LCMS applications. The 2700 Sample Manager automates sample aspiration and injection from microtiter plates, test tubes, Eppendorf tubes, or conventional autosampler vials.

    AI/ML Overview

    The provided text is a 510(k) premarket notification letter from the FDA to Waters Corporation regarding their Waters® 2700 Sample Manager device. This document primarily focuses on regulatory approval and does not contain information about acceptance criteria, device performance studies, or any of the detailed aspects of an AI/ML-driven device evaluation you've requested.

    The Waters® 2700 Sample Manager is described as a device used in a clinical laboratory to manage sample injection for high throughput LC and LCMS applications. This sounds like an instrument/hardware for laboratory automation, not an AI/ML diagnostic or prognostic device that would typically undergo the kind of performance studies or have "acceptance criteria" for accuracy, sensitivity, or specificity in the way you've outlined.

    Therefore, I cannot extract the requested information from this document. The document doesn't mention:

    • A table of acceptance criteria or reported device performance
    • Sample sizes for test sets or data provenance
    • Number or qualifications of experts for ground truth
    • Adjudication methods
    • MRMC comparative effectiveness studies
    • Standalone algorithm performance studies
    • Type of ground truth used
    • Sample size for training sets
    • How ground truth for training was established
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