Artificial intelligence-based assessment of built environment from Google Street View and coronary artery disease prevalence.

ABSTRACT:

Background and aims

Built environment plays an important role in the development of cardiovascular disease. Tools to evaluate the built environment using machine vision and informatic approaches have been limited. This study aimed to investigate the association between machine vision-based built environment and prevalence of cardiometabolic disease in US cities.

Methods

This cross-sectional study used features extracted from Google Street View (GSV) images to measure the built environment and link them with prevalence of coronary heart disease (CHD). Convolutional neural networks, linear mixed-effects models, and activation maps were utilized to predict health outcomes and identify feature associations with CHD at the census tract level. The study obtained 0.53 million GSV i

SUBMITTER: Chen Z 

PROVIDER: S-EPMC11075932 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature