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

Design Considerations for High Impact, Automated Echocardiogram Analysis

Computers and Society 2020-06-22 v2 Machine Learning

Authors: 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.

Keywords

cardiac disease prediction ecg analysis clinical prediction

Cite

@article{arxiv.2006.06292,
  title  = {Design Considerations for High Impact, Automated Echocardiogram Analysis},
  author = {Wiebke Toussaint and Dave Van Veen and Courtney Irwin and Yoni Nachmany and Manuel Barreiro-Perez and Elena Díaz-Peláez and Sara Guerreiro de Sousa and Liliana Millán and Pedro L. Sánchez and Antonio Sánchez-Puente and Jesús Sampedro-Gómez and P. Ignacio Dorado-Díaz and Víctor Vicente-Palacios},
  journal= {arXiv preprint arXiv:2006.06292},
  year   = {2020}
}

Comments

2.5 pages, ICML 2020 Machine Learning for Global Health Workshop

Design Considerations for High Impact, Automated Echocardiogram Analysis

Abstract

Keywords

Cite

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