Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.

ABSTRACT:

Importance

Built environment plays an important role in development of cardiovascular disease. Large scale, pragmatic evaluation of built environment has been limited owing to scarce data and inconsistent data quality.

Objective

To investigate the association between image-based built environment and the prevalence of cardiometabolic disease in urban cities.

Design, setting, and participants

This cross-sectional study used features extracted from Google satellite images (GSI) to measure the built environment and link them with prevalence of cardiometabolic disease. Convolutional neural networks, light gradient-boosting machines, and activation maps were used to assess the association with health outcomes and identify feature associations with coronary heart disease (C

SUBMITTER: Chen Z 

PROVIDER: S-EPMC11063925 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature