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Deep-Learning Models for the Echocardiographic Assessment of Diastolic Dysfunction.
Pandey, Ambarish; Kagiyama, Nobuyuki; Yanamala, Naveena; Segar, Matthew W; Cho, Jung S; Tokodi, Márton; Sengupta, Partho P.
Affiliation
  • Pandey A; Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Kagiyama N; Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Department of Cardiovascular Biology and Medicine, Juntendo University, Tokyo, Japan; Department of Digital Health and Telemedicine R & D, Juntendo Unive
  • Yanamala N; Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA.
  • Segar MW; Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
  • Cho JS; Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Division of Cardiology, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Tokodi M; Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA; Heart and Vascular Center, Seemelweis University, Budapest, Hungary.
  • Sengupta PP; Center for Clinical Innovation, Division of Cardiology, West Virginia University Heart and Vascular Institute, Morgantown, West Virginia, USA. Electronic address: partho.sengupta@wvumedicine.org.
JACC Cardiovasc Imaging ; 14(10): 1887-1900, 2021 10.
Article in En | MEDLINE | ID: mdl-34023263
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning / Heart Failure Type of study: Guideline / Prognostic_studies Limits: Humans Language: En Journal: JACC Cardiovasc Imaging Journal subject: ANGIOLOGIA / CARDIOLOGIA / DIAGNOSTICO POR IMAGEM Year: 2021 Document type: Article Affiliation country: Country of publication:
Fulltext
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Deep Learning / Heart Failure Type of study: Guideline / Prognostic_studies Limits: Humans Language: En Journal: JACC Cardiovasc Imaging Journal subject: ANGIOLOGIA / CARDIOLOGIA / DIAGNOSTICO POR IMAGEM Year: 2021 Document type: Article Affiliation country: Country of publication: