ArticleEuropean heart journal2024
Artificial intelligence-based assessment of built environment from Google Street View and coronary artery disease prevalence.
Article in European heart journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Green spaces, blue spaces and human health: an updated umbrella review of epidemiological meta-analyses.Frontiers in public health · 2025Pooled it
- Research opportunities to advance cardiovascular health through a planetary health lens.American journal of preventive cardiology · 2026Review
- Race and Ethnicity in Cardiovascular Disease Risk Prediction for Multiethnic Populations: Insights From Global Guidelines.JACC. Asia · 2026Review
- Street view-derived city built environment and vulnerability to temperature extremes: a nationally representative population-based cohort study.BMC medicine · 2025Article
- A Present Where AI is Enhancing Laboratory Medicine, A Future Where It Redefines Healthcare.EJIFCC · 2025Article
- 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
- SatHealth: A Multimodal Public Health Dataset with Satellite-based Environmental Factors.KDD : proceedings. International Conference on Knowledge Discovery & Data Mining · 2025Article
- Exposomic Determinants of Atherosclerosis: Recent Evidence.Current atherosclerosis reports · 2025Review
- AI-Facilitated Assessment of Built Environment Using Neighborhood Satellite Imagery and Cardiovascular Risk.Journal of the American College of Cardiology · 2024Article
- Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2024Review
- Deep Learning-Based Assessment of Built Environment From Satellite Images and Cardiometabolic Disease Prevalence.JAMA cardiology · 2024Article
- Understanding the Cardiovascular and Metabolic Health Effects of Air Pollution in the Context of Cumulative Exposomic Impacts.Circulation research · 2024Review
- Harnessing Wearables and Digital Technologies to Decode the Cardiovascular Exposome.Methodist DeBakey cardiovascular journal · 2024Review
- Big Data, Big Insights: Leveraging Data Analytics to Unravel Cardiovascular Exposome Complexities.Methodist DeBakey cardiovascular journal · 2024Review
- Zip Code Health Disparities: Mapping Cardiovascular Inequities at the Neighborhood Level.Methodist DeBakey cardiovascular journal · 2024Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND AND
aimsBuilt 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.
methodsThis 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 images covering 789 census tracts in seven US cities (Cleveland, OH; Fremont, CA; Kansas City, MO; Detroit, MI; Bellevue, WA; Brownsville, TX; and Denver, CO).
resultsBuilt environment features extracted from GSV using deep learning predicted 63% of the census tract variation in CHD prevalence. The addition of GSV features improved a model that only included census tract-level age, sex, race, income, and education or composite indices of social determinant of health. Activation maps from the features revealed a set of neighbourhood features represented by buildings and roads associated with CHD prevalence.
conclusionsIn this cross-sectional study, the prevalence of CHD was associated with built environment factors derived from GSV through deep learning analysis, independent of census tract demographics. Machine vision-enabled assessment of the built environment could potentially offer a more precise approach to identify at-risk neighbourhoods, thereby providing an efficient avenue to address and reduce cardiovascular health disparities in urban environments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.