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arxiv: 2006.06292 · v2 · pith:2DVS3EEG · submitted 2020-06-11 · cs.CY · cs.LG

Design Considerations for High Impact, Automated Echocardiogram Analysis

Wiebke Toussaint , Dave Van Veen , Courtney Irwin , Yoni Nachmany , Manuel Barreiro-Perez , Elena D\'iaz-Pel\'aez , Sara Guerreiro de Sousa , Liliana Mill\'an
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Pedro L. S\'anchez Antonio S\'anchez-Puente Jes\'us Sampedro-G\'omez P. Ignacio Dorado-D\'iaz V\'ictor Vicente-Palacios
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classification cs.CY cs.LG
keywords analysisdesigndiseaseechocardiogramheartaccountsautomateautomated
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Deep learning has the potential to automate echocardiogram analysis for early detection of heart disease. Based on a qualitative analysis of design concerns, this study suggests that predicting normal heart function instead of disease accounts for data quality bias and significantly increases efficiency in cardiologists' workflows.

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