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510(k) Data Aggregation

    K Number
    K210791
    Device Name
    Us2.v1
    Date Cleared
    2021-07-27

    (133 days)

    Product Code
    Regulation Number
    892.2050
    Reference & Predicate Devices
    Why did this record match?
    Device Name :

    Us2.v1

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

    Us2.v1 is a fully automated software platform that processes, analyses and makes measurements on acquired transthoracy cardiac ultrasound images, automatically producing a full report with measurements of several and functional parameters. The data produced by this software is intended to be used to support qualified cardiologists or licensed primary care providers for clinical decision-making. Us2.v1 is in adult patients. Us2.v1 has not been validated for the assessment of congenital heart disease, pericardial disease, and/or intra-cardiac lesions (e.g. tumours, thrombi).

    Device Description

    Us2.v1 is an image post-processing analysis software device used for viewing and quantifying cardiovascular ultrasound images in DICOM format. The device is intended to aid diagnostic review and analysis of echocardiographic data, patient record management and reporting.

    The software provides an interface for a skilled sonographer to perform the necessary markup on the echocardiographic image prior to review by the prescribing physician. The markup includes: the cardiac segments captured, measurements of distance, time, area and blood flow, quantitative analysis of cardiac function, and a summary report.

    The software allows the sonographer to enter their markup manually. It also provides automated markup and analysis, which the sonographer may choose to accept outright, to accept partially and modify, or to reject and ignore. Machine learning based view classification and border detection form the basis for this automated analysis. Additionally, the software has features for organizing, displaying and comparing to reference guidelines the quantitative data from cardiovascular images acquired from ultrasound scanners.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    The documents state a single, overarching acceptance criterion:

    Acceptance CriterionReported Device Performance
    Non-inferiority margin (Δ=0.25) for the reference-scaled individual equivalence coefficient (IEC) such that `IEC + 1.96 * SD(IEC)
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