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arXiv · 2006.06292

Design Considerations for High Impact, Automated Echocardiogram Analysis

Wiebke Toussaint·Dave Van Veen·Courtney Irwin·Yoni Nachmany·Manuel Barreiro-Perez·Elena Díaz-Peláez·Sara Guerreiro de Sousa·Liliana Millán·Pedro L. Sánchez·Antonio Sánchez-Puente·Jesús Sampedro-Gómez·P. Ignacio Dorado-Díaz·Víctor Vicente-Palacios
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Abstract

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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Wiebke Toussaint, Dave Van Veen, Courtney Irwin, Yoni Nachmany, Manuel Barreiro-Perez, Elena Díaz-Peláez, Sara Guerreiro de Sousa, Liliana Millán, Pedro L. Sánchez, Antonio Sánchez-Puente, Jesús Sampedro-Gómez, P. Ignacio Dorado-Díaz, Víctor Vicente-Palacios. 2020-06-11. Design Considerations for High Impact, Automated Echocardiogram Analysis. https://arxiv.org/abs/2006.06292

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