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  • 1st Free Software for AI-driven Automatic CT Analysis of COVID19 Pneumonia
  • The old version( of MEDIP COVID19 does not work. Please use the latest version.

    We break through the global pandemic 'COVID-19'
    with AI technology

    Institutions download
    Countries download

    2020-09-26 10:19:52 AM Current date (UTC+09:00)


    The AI automatically calculates a physical weight (gram) of pneumonia, as well as the extent (%), which is in accord with the use case of ACR(American College of Radiology)'s Data Science Institute. Users can segment and analyze COVID19 pneumonia on CT with simple commands.


    Installation & User Guide

    This software works on Windows7 64bit or higher operating system and NVIDIA graphics card(with 2GB GPU memory or higher) supporting CUDA and latest drivers.
    Please make sure of the update of the GPU driver with the latest version before use.

    Environment Detail : OS-Microsoft Windows 7(64bit) or higher, CPU-Intel i5 or higher, RAM-8GB or higher, GPU-NVIDIA GeForce 1000series or higher(with 2GB GPU memory or higher), Network environment with Internet access
    Please contact us (COVID19@medicalip.com) if you have any problems or do not have any computing resources.



    MEDIP COVID19 v1.2.1.0 (released 2020/09/17)

    - Bugfix on interface

    MEDIP COVID19 v1.2.0.0 (released 2020/04/27)

    - Improved reinforcement learning model

    MEDIP COVID19 v1.1.0.2 (released 2020/04/08)

    - Bugfix on interface and improved on analysis features

    MEDIP COVID19 v1.1.0.1 (released 2020/04/06)

    - GPU hardware exception
    - MIP Extension link and bugfix

    MEDIP COVID19 v1.1.0.0 (released 2020/03/30)

    - Improved reinforcement learning model
    - Improved report template
    - Bugfix on interface and analysis

    MEDIP COVID19 v1.0.1.0 (released 2020/03/20)

    - Improved report template
    - Improved installer and license dialog
    - Improved 3D opacity visualization

    MEDIP COVID19 v1.0.0.1 (released 2020/03/19)

    - Bugfix on report tab update
    - GPU hardware inspection exception handling
    - Improved 2D visualization

    MEDIP COVID19 v1.0.0.0 (released 2020/03/18)

    - DICOM(CT) loading
    - Automatic lung, pneumonia segmentation by deep neural network
    - Automatic feature extraction: volume, mean HU, standard deviation, pneumonia burden, % extent
    - Automatic report generation


    The manuscript dealing with the technique for this AI application was submitted to the journal on April 7. The software currently can be used for research purposes only. We are waiting for collaborative researchers to verify the value of whole-lesion quantification on CT in COVID19 (contact: COVID19@medicalip.com).


    Model Performance

    The intraclass correlation coefficients for % extent and weight of pneumonia between AI and human expert were 0.990 and 0.993, respectively, in the internal validation dataset.
    v1.2.0.0: 0.990, 0.993
    v1.1.0.0: 0.987, 0.992
    v1.0.0.0: 0.962, 0.924



    Please cite the MEDIP as follows: MEDIP COVID19 v1.2.1.0 (MEDICALIP, Co. Ltd. Seoul, Korea)



    1. Extension of Coronavirus Disease 2019 (COVID-19) on Chest CT and Implications for Chest Radiograph Interpretation
    Hyewon Choi, Xiaolong Qi, Soon Ho Yoon, Sang Joon Park, Kyung Hee Lee, Jin Yong Kim, Young Kyung Lee, Hongseok Ko, Ki Hwan Kim, Chang Min Park, Yun-Hyeon Kim, Junqiang Lei, Jung Hee Hong, Hyungjin Kim, Eui Jin Hwang, Seung Jin Yoo, Ju Gang Nam, Chang Hyun Lee, Jin Mo Goo
    Radiology: Cardiothoracic Imaging. Vol. 2, No. 2, Mar 30 2020 See in detail

    2. Automatic CT Quantification of Coronavirus Disease 2019 pneumonia: An international collaborative development, validation, and clinical implication
    Seung-Jin Yoo, Xiaolong Qi, Shohei Inui, Sang Joon Park, Hyungjin Kim, Yeon Joo Jeong, Kyung Hee Lee, Young Kyung Lee, Bae Young Lee, Jin Yong Kim, Kwang Nam Jin, Jae-Kwang Lim, Yun-Hyeon Kim, Ki Beom Kim, Zicheng Jiang, Chuxiao Shao, Junqiang Lei, Shengqiang Zou, Hongqiu Pan, Ye Gu, Guo Zhang, Jin Mo Goo, Soon Ho Yoon
    Preprint, Jul 24 2020 See in detail

    3. Prognostic Implication of Volumetric Quantitative CT Analysis in Patients with COVID-19: A Multicenter Study in Daegu, Korea
    Byunggeon Park, Jongmin Park, Jae-Kwang Lim, Kyung Min Shin, Jaehee Lee, Hyewon Seo, Yong Hoon Lee, Jun Heo, Won Kee Lee, Jin Young Kim, Ki Beom Kim, Sungjun Moon, Sooyoung Choi
    Korean J Radiol. 2020;21:e130, Aug 04 2020 See in detail

    4. Anterior Pulmonary Ventilation Abnormalities in COVID-19
    Soon Ho Yoon, Minsuok Kim
    RSNA. Published Online: Aug 13 2020 See in detail

    5. Predictive Parameters for the Worsening Clinical Course of Mild COVID-19 Pneumonia
    Cho Rom Hahm, Young Kyung Lee, Dong Hyun Oh, Mi Young Ahn, Jae-Phil Choi, Na Ree Kang, Jungkyun Oh, Hanzo Choi, Suhyun Kim
    Preprint, Aug 7 2020 See in detail



    This project was initiated by the proposal of the CHESS-Korea Deep Neural Networks of CT Imaging for COVID19 with the support of MEDICALIP. List of current contributors is as follows and more contributors will be added:
    Xiaolong Qi, The First Hospital of Lanzhou University, China and Soon Ho Yoon, Seoul National University Hospital, Korea
    Seung-Jin Yoo, Hanyang University Medical Center, Korea
    Shohei Inui, Japan Self-Defense Forces Central Hospital, Japan
    Yeon Joo Jeong, Pusan National University Hospital, Korea
    Kyung Hee Lee, Seoul National University Bundang Hospital, Korea
    Young Kyung Lee, Seoul Medical Center, Korea
    Bae Young Lee, Eunpyeong St. Mary's Hospital, Korea
    Jin Yong Kim, Incheon Medical Center, Korea
    Kwang Nam Jin, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Korea
    Jae-Kwang Lim, Kyungpook National University Hospital, Korea
    Yun-Hyeon Kim, Chonnam National University Hospital, Korea
    Ki Beom Kim, Daegu Fatima Hospital, Korea
    Zicheng Jiang, Ankang Central Hospital, China
    Chuxiao Shao, Lishui Central Hospital, China
    Junqiang Lei, The First Hospital of Lanzhou University, China
    Shengqiang Zou, The Affiliated Third Hospital of Jiangsu University, China
    Hongqiu Pan, The Affiliated Third Hospital of Jiangsu University, China
    Yuki Himoto, Kyoto University Hospital, Japan
    Akihiko Sakata, Wakayama Red Cross Hospital, Japan
    Charlene Liew Jin Yee, Changi General Hospital, Singapore
    Bin Song, West China Hospital, Sichuan University, China
    Yuntian Chen, West China Hospital, Sichuan University, China
    HM Hospitales, Spain
    Eun Kyoung Hong, Netherlands Cancer Institute, Netherlands


    Quantifying COVID19 on chest X-ray

    Our next step is to develop free software for quantifying COVID19 on chest X-ray radiographs in two months. If you agree with our initiative, please join our step as a co-researcher.

    Any resource support or collaboration is welcome to speed up this global pandemic troubleshooting.

  • Company Name ㅣ MEDICALIP
    CEO ㅣ Sang Joon Park    Tel ㅣ +82-2-2135-9148
    Business Registration Number ㅣ 406-81-04085
    Address ㅣ 7F, Changgyeong Building, 174, Yulgok-ro, Jongno-gu, Seoul, Republic of Korea
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