Design Considerations for High Impact, Automated Echocardiogram Analysis

11 Jun 2020Wiebke ToussaintDave Van VeenCourtney IrwinYoni NachmanyManuel Barreiro-PerezElena Díaz-PeláezSara Guerreiro de SousaLiliana MillánPedro L. SánchezAntonio Sánchez-PuenteJesús Sampedro-GómezP. Ignacio Dorado-DíazVíctor Vicente-Palacios

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...

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet