Design Considerations for High Impact, Automated Echocardiogram Analysis

Antonio S\'anchez-Puente; Courtney Irwin; Dave Van Veen; Elena D\'iaz-Pel\'aez; Jes\'us Sampedro-G\'omez; Liliana Mill\'an; Manuel Barreiro-Perez; Pedro L. S\'anchez; P. Ignacio Dorado-D\'iaz; Sara Guerreiro de Sousa

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Design Considerations for High Impact, Automated Echocardiogram Analysis

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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.

Design Considerations for High Impact, Automated Echocardiogram Analysis

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