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510(k) Data Aggregation

    K Number
    K050376
    Device Name
    PROWIN
    Date Cleared
    2005-04-07

    (52 days)

    Product Code
    Regulation Number
    892.2050
    Reference & Predicate Devices
    Predicate For
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    The Prowin™ software program is a Windows-based application used on a personal computer intended for viewing carotid ultrasound images and measuring arterial wall thickness. It is specifically indicated for the measurement of carotid artery far and near wall intima-media thickness (IMT) from images obtained from an ultrasound system. A physician may use this information in conjunction with other medical data in the assessment of a patient's cardiovascular risk.

    Device Description

    Prowin™ is a software program designed to measure carotid artery intimamedia thickness from ultrasound images. Prowin™ operates on a stand-alone personal computer running a Microsoft Windows™ operating system. Carotid artery images from any ultrasound machine are transferred to the computer running Prowin™ by way of any media. Following the transfer of images, Prowin™ uses proprietary, patent-pending algorithms to measure intima-media thickness (IMT) of the near and far walls of the carotid artery. The Prowin™ software also has the capability to generate a report indicating the IMT value.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and study information for K050376, Prowin™ Medical Image Measurement Software, based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    The provided 510(k) summary does not explicitly state acceptance criteria in terms of performance metrics (e.g., minimum accuracy, maximum deviation). Instead, it focuses on demonstrating measurement repeatability as an indicator of performance.

    CharacteristicAcceptance Criteria (Implied)Reported Device Performance
    MeasurementCoefficient of variation < 1%Coefficient of variation < 1%
    Repeatability

    2. Sample Size and Data Provenance for the Test Set

    • Sample Size: The document does not specify the sample size used for performance testing. It only mentions "data collected using Prowin™."
    • Data Provenance: Not specified. It's unclear if the data was retrospective or prospective, or from what country.

    3. Number of Experts for Ground Truth and Qualifications

    This information is not provided in the document. The text does not mention the use of experts to establish a ground truth for performance testing.

    4. Adjudication Method for the Test Set

    This information is not provided in the document.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

    • Was it done? No, a MRMC comparative effectiveness study is not mentioned in the document.
    • Effect size: Not applicable, as no such study was reported.

    6. Standalone (Algorithm Only) Performance Study

    Yes, a standalone performance study was done. The performance testing section states: "As an indicator of measurement repeatability, data collected using Prowin™ as an automated measurement model the coefficient of variation to be <1%." This directly refers to the device's algorithmic performance.

    7. Type of Ground Truth Used

    The document does not explicitly state the type of ground truth used. Given the focus on "measurement repeatability" and the lack of mention of human experts or other objective standards like pathology, it's highly likely that the "ground truth" for repeatability was established by the device's own repeated measurements on the same image data, or perhaps by comparing its measurements to a manually (but not necessarily expert-adjudicated) derived set of measurements. However, this is an inference; the document does not clarify.

    8. Sample Size for the Training Set

    This information is not provided in the document. The submission is for software that measures IMT using "proprietary, patent-pending algorithms," but it does not detail the development process of these algorithms, including whether they involved machine learning with a separate training set.

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

    This information is not provided in the document, as details about a training set or its ground truth are absent.

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