ArticleJournal of the American College of Cardiology2024
AI-Facilitated Assessment of Built Environment Using Neighborhood Satellite Imagery and Cardiovascular Risk.
Article in Journal of the American College of Cardiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Research opportunities to advance cardiovascular health through a planetary health lens.American journal of preventive cardiology · 2026Review
- Environment and CVD: moving from Risk Prediction to Risk Management.Current atherosclerosis reports · 2026Review
- Emerging AI tools for geospatially resolved cardiovascular risk.Nature reviews. Cardiology · 2025Article
- AI-Enhanced Analysis of Built Environment Imagery and Neighborhood Obesity in US Cities.JAMA network open · 2025Article
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9 authors.
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Abstract
backgroundBuilt environment affects cardiovascular health, but comprehensive assessment in a scalable fashion, for population health and resource allocation, is constrained by limitations of current microscale measures.
objectivesThe purpose of this study was to investigate the association between satellite image-based environment and risk of major adverse cardiovascular events (MACE).
methodsUsing a pretrained deep neural network, features depicting the built environment from Google Satellite Imagery (GSI) around 64,230 patients in Northern Ohio undergoing coronary artery calcium (CAC) scoring were extracted. Elastic net regularized Cox proportional hazards models identified associations of GSI features with MACE risk (defined as myocardial infarction, stroke, heart failure, or death). A composite GSI risk score was constructed using features that demonstrated nonzero coefficients in the elastic net model. We assessed association of this score with MACE risk, after adjusting for CAC scores and the social vulnerability index (SVI). Its interactions with CAC scores were also examined in subgroups.
resultsAdjusting for CAC and traditional risk factors, the GSI risk score was significantly associated with higher MACE risk (HR: 2.67; 95% CI: 1.63-4.38; P < 0.001). However, adding SVI reduced this association to nonsignificance (HR: 1.54; 95% CI: 0.91-2.60; P = 0.11). Patients in the highest quartile (Q4) of GSI risk score had a 56% higher observed risk of MACE (HR: 1.56; 95% CI: 1.32-1.86; P < 0.005) compared with the lowest quartile (Q1). The GSI risk score had the strongest association with MACE risk in patients with CAC = 0. This association was attenuated, but remained significant, with higher CAC.
conclusionsAI-enhanced satellite images of the built environment were linked to MACE risk, independently of traditional risk factors and CAC, but this was influenced by social determinants of health, represented by SVI. Satellite image-based assessment of the built environment may provide a rapid scalable integrative approach, warranting further exploration for enhanced risk prediction.
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