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510(k) Data Aggregation

    K Number
    K070603
    Date Cleared
    2007-03-20

    (15 days)

    Product Code
    Regulation Number
    890.3800
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    C.T.M. MOBILITTY SCOOTER # HS-295

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

    The C.T.M. Mobility Scooter HS-295 is an indoor/outdoor scooter that provides transportation for a disabled or elderly person.

    Device Description

    The C.T.M. Mobility Scooter HS-295 is an indoor/outdoor scooter that is battery operated. It has a base with four wheels, a padded seat with adjustable armrests, and hand controls at the top of the steering column which allow the rider to control the movement of the scooter. It can be disassembled for transport and is provided with an on-board battery charger.

    AI/ML Overview

    The provided text is related to a 510(k) submission for a mobility scooter, which is a medical device. However, this document does not contain information about acceptance criteria or a study that proves the device meets those criteria in the context of an AI/ML medical device.

    Here's why and what information is missing:

    Why the provided text is not relevant to the requested information:

    • Device Type: The device is a "C.T.M. Mobility Scooter HS-295," an electric scooter for transportation. This is a mechanical device, not an AI/ML software or algorithm.
    • Testing Conducted: The summary explicitly states: "Comparative performance testing and clinical evaluations were not submitted as part of this 510(k)." It mentions that "Tests listed in the Guidance Document for the Preparation of Premarket Notification [510(k)] Applications for Mechanical and Scooters, and Motorized Three Wheeled Vehicles, July 1995, were conducted." This refers to standard mechanical and safety testing for such vehicles, not performance metrics for an AI/ML algorithm.
    • Lack of AI/ML Specifics: There is no mention of algorithms, machine learning, deep learning, diagnostic accuracy, image analysis, or any other characteristic of an AI/ML medical device.

    Therefore, I cannot provide the requested information based on the input text. The questions you've asked (about acceptance criteria for an AI/ML model, sample sizes for test/training sets, expert ground truth, MRMC studies, standalone performance, etc.) are specifically geared towards the evaluation of AI/ML software as a medical device. This document describes a physical mobility scooter.

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