Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging.

Journal: Nature medicine
Published Date: May 13, 2024

Abstract

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

Authors

  • Yan-Ran Joyce Wang
    Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, 725 Welch Road, CA, 94304, Stanford, USA.
  • Kai Yang
    Department of Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yi Wen
    School of Public Health and Management, Research Center for Medicine and Social Development, Innovation Center for Social risk Governance in Health, Chongqing Medical University, Chongqing 400016, China.
  • Pengcheng Wang
    Department of Plant Protection, Henan Institute of Science and Technology, Xinxiang, China.
  • Yuepeng Hu
    Department of Electrical and Computer Engineering, Duke University, Durham, NC, USA.
  • Yongfan Lai
    School of Engineering, University of Science and Technology of China, Hefei, China.
  • Yufeng Wang
    People's Hospital of Gaoxin, 768 Fudong Road, Weifang 261205, China.
  • Kankan Zhao
    Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, SZ University Town, Shenzhen, 518055, China.
  • Siyi Tang
    Department of Electrical Engineering, Stanford University, Stanford, CA.
  • Angela Zhang
    Department of Genetics, Stanford University, Stanford, CA, USA.
  • Huayi Zhan
    Changhong AI Research (CHAIR), Sichuan Changhong Electronics Holding Group, Mianyang, China.
  • Minjie Lu
    Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
  • Xiuyu Chen
    Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China.
  • Shujuan Yang
    Shandong Institute for Food and Drug Control, Jinan 250101, China.
  • Zhixiang Dong
    Department of Magnetic Resonance Imaging, Fuwai Hospital and National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Yining Wang
    Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
  • Hui Liu
    Institute of Urology and Nephrology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China.
  • Lei Zhao
    Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming, China.
  • Lu Huang
    School of Food Science and Technology, Dalian Polytechnic University, National Engineering Research Center of Seafood, Dalian 116034, PR China.
  • Yunling Li
    The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
  • Lianming Wu
    Renji Hospital, Shanghai, China.
  • Zixian Chen
    Department of Radiology, The First Hospital of Lanzhou University, Lanzhou, Gansu, China.
  • Yi Luo
    Electrical and Computer Engineering Department, Bioengineering Department, University of California, Los Angeles, CA 90095 USA, and also with the California NanoSystems Institute, University of California, Los Angeles, CA 90095 USA.
  • Dongbo Liu
    Changhong AI Research (CHAIR), Sichuan Changhong Electronics Holding Group, Mianyang, China.
  • Pengbo Zhao
  • Keldon Lin
    Mayo Clinic Alix School of Medicine, Phoenix, AZ, USA.
  • Joseph C Wu
  • Shihua Zhao
    Department of Magnetic Resonance Imaging, Fuwai Hospital, State Key Laboratory of Cardiovascular Disease, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China. cjrzhaoshihua2009@163.com.

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