Artificial intelligence-enhanced comprehensive assessment of the aortic valve stenosis continuum in echocardiography.

Journal: EBioMedicine
PMID: 39842286

Abstract

BACKGROUND: Transthoracic echocardiography (TTE) is the primary modality for diagnosing aortic stenosis (AS), yet it requires skilled operators and can be resource-intensive. We developed and validated an artificial intelligence (AI)-based system for evaluating AS that is effective in both resource-limited and advanced settings.

Authors

  • Jiesuck Park
    Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, South Korea.
  • Jiyeon Kim
    Department of Statistics, Keimyung University, Daegu, Republic of Korea.
  • Jaeik Jeon
    CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, 03 721, South Korea.
  • Yeonyee E Yoon
    Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.).
  • Yeonggul Jang
  • Hyunseok Jeong
  • Youngtaek Hong
    CONNECT-AI R&D Center, Yonsei University College of Medicine.
  • Seung-Ah Lee
    CONNECT-AI Research Center, Yonsei University College of Medicine, Seoul, Republic of Korea; Ontact Health Inc., Seoul, Republic of Korea.
  • Hong-Mi Choi
    Department of Cardiology, Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam.
  • In-Chang Hwang
    Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.).
  • Goo-Yeong Cho
    Department of Internal Medicine, Seoul National University Bundang Hospital, Gyeonggi-do (I.-C.H., Y.E.Y., G.-Y.C.).
  • Hyuk-Jae Chang
    Department of Cardiology, Yonsei University College of Medicine, Seoul, Republic Of Korea.

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