Deep Learning Analysis of Google Street View to Assess Residential Built Environment and Cardiovascular Risk in a U.S. Midwestern Retrospective Cohort.

AIMS

Cardiovascular disease (CVD) is a leading global cause of mortality. Environmental factors are increasingly recognized as influential determinants of cardiovascular health. Nevertheless, a finer-grained understanding of the effects of the built environment remains crucial for comprehending CVD. We sought to investigate the relationship between built environment features, including residential greenspace and sidewalks, and cardiovascular risk using street-level imagery and deep learning techniques.

METHODS

This study employed Google Street View (GSV) imagery and deep learning techniques to analyze built environment features around residences in relation to major adverse cardiovascular events (MACE) risk. Data from a Northeast Ohio cohort were utilized. Various covariates, including socioeconomic and environmental factors, were incorporated in Cox Proportional Hazards models.

RESULTS

Of 49,887 individuals included, 2,083 experienced MACE over a median follow-up of 26.86 months. Higher tree-sky index and sidewalk presence were associated with reduced MACE risk (HR: 0.95, 95% CI: 0.91-0.99, and HR: 0.91, 95% CI: 0.87-0.96, respectively), even after adjusting for demographic, socioeconomic, environmental, and clinical factors.

CONCLUSIONS

Visible vertical greenspace and sidewalks, as discerned from street-level images using deep learning, demonstrated potential associations with cardiovascular risk. This innovative approach highlights the potential of deep learning to analyze built environments at scale, offering new avenues for public health research. Future research is needed to validate these associations and better understand the underlying mechanisms.