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510(k) Data Aggregation

    K Number
    K173360
    Device Name
    Athena 190
    Date Cleared
    2018-05-17

    (203 days)

    Product Code
    Regulation Number
    890.3850
    Reference & Predicate Devices
    N/A
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The intended use of the Athena Manual Wheelchair is to provide mobility to a patient restricted to a sitting position. The Athena Manual Wheelchair in not designed, sold, or intended for use except as indicated.

    Device Description

    Not Found

    AI/ML Overview

    The provided text is a 510(k) summary from the FDA for a manual wheelchair called "Athena 190". It is a regulatory document affirming substantial equivalence to a predicate device, not a performance study report for an AI/ML device. Therefore, it does not contain the information requested regarding acceptance criteria, device performance, test set details, expert qualifications, adjudication methods, MRMC studies, standalone performance, training set details, or ground truth establishment relevant to AI/ML devices.

    The document states:

    • Trade/Device Name: Athena 190
    • Regulation Name: Mechanical Wheelchair
    • Regulatory Class: Class I
    • Product Code: IOR
    • Indications for Use: To provide mobility to a patient restricted to a sitting position.

    Since the device is a mechanical wheelchair and not an AI/ML diagnostic or assistive technology, the standard acceptance criteria and study designs typically applied to AI/ML devices (e.g., sensitivity, specificity, AUC, human reader improvement) are not applicable or described in this type of regulatory submission. The FDA's 510(k) clearance process for a Class I mechanical device primarily focuses on demonstrating substantial equivalence to a legally marketed predicate device, ensuring the new device is as safe and effective as the existing one. This typically involves performance testing related to physical properties and safety rather than diagnostic accuracy or human-AI interaction in a clinical reading environment.

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