Built Environment Features Obtained from Google Street View Are Associated with Coronary Artery Disease Prevalence: A Deep-Learning Framework.

Zhuo ChenYassin KhalifaJean-Eudes DazardIssam Motairek Sanjay RajagopalanSadeer Al-Kindi

Published in: medRxiv : the preprint server for health sciences (2023)

In this cross-sectional study, a significant portion of CHD prevalence were explained by GSV-based built environment factors analyzed using deep learning, independent of census tract demographics. Machine vision enabled assessment of the built environment could help play a significant role in designing and improving heart-heathy cities.

Keyphrases

deep learning
coronary artery disease
risk factors
artificial intelligence
convolutional neural network
heart failure
machine learning
percutaneous coronary intervention
coronary artery bypass grafting
cardiovascular disease
atrial fibrillation
cardiovascular events