Machine Learning Computer Vision Point of Care Decision Support of Echocardiographic Identification of Hypertrophic Cardiomyopathy.

Sreekanth Vemulapalli¹, Fawaz Alenezi², Hyeon Jeong³, Anna Giczewska⁴, Karen Chiswell⁴, Pamela Douglas⁵, Anru R Zhang⁶, Svati Shah⁷, Ricardo Henao⁸, Andrew Wang²

¹Duke Clinical Research Institute, Durham, North Carolina, USA; Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA. Electronic address: sreekanth.vemulapalli@duke.edu.

²Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA.

³Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA.

⁴Duke Clinical Research Institute, Durham, North Carolina, USA.

⁵Duke Clinical Research Institute, Durham, North Carolina, USA; Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA.

⁶Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA; Department of Computer Science, Duke University, Durham, North Carolina, USA.

⁷Duke Clinical Research Institute, Durham, North Carolina, USA; Division of Cardiology, Duke University Medical Center, Durham, North Carolina, USA; Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA; Institute of Molecular Physiology, Duke University, Durham, North Carolina, USA.

⁸Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA; King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

PubMed PMC DOI PDF

Abstract

BACKGROUND: Hypertrophic cardiomyopathy (HCM) remains underdiagnosed, and artificial intelligence tools for echocardiographic recognition have been hampered by lack of insight into drivers of model predictions and ease of implementation. OBJECTIVES: The purpose of this study was to train and validate a machine learning model with visualization of model prediction, optimized for implementation, to…

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