AI-Enhanced Analysis of Built Environment Imagery and Neighborhood Obesity in US Cities

Is artificial intelligence–enhanced geospatial image analysis of built environment features associated with improved estimates of neighborhood obesity prevalence in US cities beyond traditional factors?This cross-sectional study of 14 413 census tracts within 100 US cities found that integrating geospatial image features from more than 94 000 satellite and more than 670 000 street view images with traditional factors in a linear mixed-effects model was associated with improved obesity prevalence variance explanation.In this study, artificial intelligence–driven geospatial image analysis was associated with enhanced obesity prevalence estimation, which may inform targeted public health and urban planning interventions.

Corresponding Authors: Sanjay Rajagopalan, MD, Harrington Heart and Vascular Institute, University Hospitals, 11100 Euclid Ave, Cleveland, OH 44106 (sanjay.rajagopalan@uhhospitals.org); Sadeer Al-Kindi, MD, Houston Methodist DeBakey Heart and Vascular Center, 6550 Fannin St, Houston, TX 77030 (sal-kindi@houstonmethodist.org).

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