LogoFDA 510(k) Search
Search Filters

Search Results

Found 1 results

510(k) Data Aggregation

    K Number
    K223556
    Device Name
    DeepCatch
    Manufacturer
    MEDICALIP Co., Ltd
    Date Cleared
    2023-06-16

    (200 days)

    Product Code
    QIH
    Regulation Number
    892.2050
    Type
    Traditional
    Panel
    Radiology
    Reference & Predicate Devices
    K191026,K130542
    Predicate For
    N/A
    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    DeepCatch analyzes CT images and auto-segments anatomical structures (skin, bone, muscle, visceral fat, subcutaneous fat, internal organs and central nervous system). Then, its volume and proportions are calculated and provided with the relevant 3D model.

    By using DeepCatch, it is possible to obtain accurate values for the volume and proportion of each anatomical structures by secondary utilization of CT images obtained for various purposes in the medical field. The type of input data is whole body CT. This device is intended to be used in conjunction with professional clinical judgement. The physician is responsible for inspecting and confirming all results.

    Device Description

    DeepCatch is medial image processing software that provides 3D reconstruction and visualization of ROI, advanced image quality improvement, auto segmentation for specific target, texture analysis, etc. Data that accurately analyzes the amount of skeletal muscle and adipose tissue distributed in the body in 3D can be used as base data in various fields.

    AI/ML Overview

    Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text.

    DeepCatch Device Performance Study Analysis

    1. Table of Acceptance Criteria and Reported Device Performance

    The acceptance criteria are implicitly defined by the null and alternative hypotheses for the performance tests. The reported device performance is the outcome of these tests.

    Test Type & MetricAcceptance Criteria (Alternative Hypothesis)Reported Device Performance
    Internal Datasets (n=100)
    DSC (between GT & DeepCatch segmentation results)Group's DSC mean ≥ 0.900DSC means ≥ 90% (met)
    External Datasets (n=580)
    DSC (between GT & DeepCatch segmentation results)Group's DSC mean ≥ 0.900DSC mean > 90% in all areas (met)
    Volume (Difference between GT & DeepCatch measurement results)Mean of within-group difference 90% (met)
    VolumeMean of within-group difference
    Ask a Question

    Ask a specific question about this device

    Page 1 of 1