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510(k) Data Aggregation

    K Number
    K051518
    Device Name
    UNITY NETWORK ID
    Date Cleared
    2005-07-08

    (30 days)

    Product Code
    Regulation Number
    870.2300
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Unity Network ID is indicated for use in data collection and clinical information management through networks with independent bedside devices. The Unity Network ID is not intended for monitoring purposes, nor is the Unity Network ID intended to control any of the clinical devices (independent bedside devices/ information systems) it is connected to.

    Device Description

    The Unity Network ID system communicates patient data from sources other than GE Medical Systems Information Technologies equipment to a clinical information system, central station, and/or GE Medical Systems Information Technologies patient monitors. The Unity Network ID acquires digital data from eight serial ports, converts the data to Unity Network protocols, and transmits the data over the monitoring network to a Unity Network device such as a patient monitor, clinical information system or central station.

    AI/ML Overview

    The information provided indicates that the Unity Network ID system underwent various quality assurance measures and testing, but it does not specify acceptance criteria in terms of performance metrics or a detailed study demonstrating device performance against such criteria. The document states that the testing "demonstrated that the Unity Network ID is as safe, as effective, and performs as well as the predicate devices," implying a comparison, but lacks the quantitative details usually found in a performance study summary.

    Therefore, the following information cannot be fully extracted based on the provided text:

    • A table of acceptance criteria and reported device performance.
    • Sample size used for the test set and data provenance.
    • Number of experts used to establish ground truth and their qualifications.
    • Adjudication method for the test set.
    • Details of a multi-reader, multi-case (MRMC) comparative effectiveness study, including effect size.
    • Details of a standalone performance study.
    • The type of ground truth used.
    • Sample size for the training set.
    • How the ground truth for the training set was established.

    Acceptance Criteria and Device Performance:

    The document describes the following quality assurance measures:

    • Risk Analysis
    • Requirements Reviews
    • Design Reviews
    • Testing on unit level (Module verification)
    • Integration testing (System verification)
    • Final acceptance testing (Validation)
    • Performance testing
    • Safety testing
    • Environmental testing

    The acceptance criteria are implicitly that the device performs "as well as the predicate devices" in terms of safety and effectiveness, and that it complies with "voluntary standards as detailed in Section 9 of this submission" (though Section 9 is not provided).

    The reported device performance is a general statement: "The results of these measurements demonstrated that the Unity Network ID is as safe, as effective, and performs as well as the predicate devices." No specific quantitative performance metrics are provided.

    Other Information:

    Due to the lack of specific detail in the provided text, the following cannot be answered directly:

    • Sample size used for the test set and the data provenance: Not provided.
    • 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 provided.
    • Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not provided.
    • 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 provided. The device described appears to be a data communication system, not an AI-powered diagnostic device that assists human readers.
    • If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not explicitly detailed, but "Performance testing" and "Final acceptance testing (Validation)" were performed. However, specific results or methodology are not given.
    • The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not provided.
    • The sample size for the training set: Not provided.
    • How the ground truth for the training set was established: Not provided.
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