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      Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography.

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          Abstract

          Machine-learning models may aid cardiac phenotypic recognition by using features of cardiac tissue deformation.

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          Author and article information

          Journal
          J. Am. Coll. Cardiol.
          Journal of the American College of Cardiology
          Elsevier BV
          1558-3597
          0735-1097
          Nov 29 2016
          : 68
          : 21
          Affiliations
          [1 ] Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York.
          [2 ] Institute of Next Generation Healthcare, Department of Genetics and Genomic Sciences, Mount Sinai Health System, New York, New York.
          [3 ] Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Internal Medicine, Medical Division, National Research Center, Cairo, Egypt.
          [4 ] Zena and Michael A. Weiner Cardiovascular Institute, Icahn School of Medicine at Mount Sinai, New York, New York. Electronic address: partho.sengupta@mountsinai.org.
          Article
          S0735-1097(16)36250-7
          10.1016/j.jacc.2016.08.062
          27884247
          4164f84f-5322-4214-b5be-762ec8c4193e
          History

          cardiomyopathy,decision support systems,left ventricular hypertrophy,speckle-tracking echocardiography

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