Design Considerations for High Impact, Automated Echocardiogram Analysis

06/11/2020

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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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Design Considerations for High Impact, Automated Echocardiogram Analysis

Predicting Heart Disease and Reducing Survey Time Using Machine Learning Algorithms

Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases

Early heart disease prediction using hybrid quantum classification

Deep Mask For X-ray Based Heart Disease Classification

Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

On Deep Neural Networks for Detecting Heart Disease

Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization

Design Considerations for High Impact, Automated Echocardiogram Analysis

Related Research

Predicting Heart Disease and Reducing Survey Time Using Machine Learning Algorithms

Machine Learning-Based Classification Algorithms for the Prediction of Coronary Heart Diseases

Early heart disease prediction using hybrid quantum classification

Deep Mask For X-ray Based Heart Disease Classification

Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening

On Deep Neural Networks for Detecting Heart Disease

Heart Attack Classification System using Neural Network Trained with Particle Swarm Optimization