ArticleEnvironment international2019
A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology.
Article in Environment international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of empirical and simulation studies evaluating the health impact of transportation interventions.Environmental research · 2020Pooled it
- Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis.The Yale journal of biology and medicine · 2023Trial
- Advancing Long-Term Outdoor Air Pollution Exposure Assessment for Health Studies: Lessons from a Multinational Research Initiative.Current environmental health reports · 2026Review
- Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses' Health Study.Environmental epidemiology (Philadelphia, Pa.) · 2026Article
- Artificial Intelligence in Environment and Human Health: Progress, Opportunities and Challenges.Current environmental health reports · 2025Review
- Developing Nationwide Estimates of Built Environment Quality Characteristics Using Street-View Imagery and Computer Vision.Environmental science & technology · 2025Article
- A review of geospatial exposure models and approaches for health data integration.Journal of exposure science & environmental epidemiology · 2025Review
- Greenspace and depression incidence in the US-based nationwide Nurses' Health Study II: A deep learning analysis of street-view imagery.Environment international · 2025Article
- Assessing greenspace and cardiovascular health through deep-learning analysis of street-view imagery in a cohort of US children.Environmental research · 2025Article
- Enhancing vector control: AI-based identification and counting of Aedes albopictus (Diptera: Culicidae) mosquito eggs.Parasites & vectors · 2024Article
- Satellite-Based and Street-View Green Space and Adiposity in US Children.JAMA network open · 2024Article
- Assessing the built environment through photographs and its association with obesity in 21 countries: the PURE Study.The Lancet. Global health · 2024Article
- Long-Term Exposure to Outdoor Ultrafine Particles and Black Carbon and Effects on Mortality in Montreal and Toronto, Canada.Research report (Health Effects Institute) · 2024Article
- Urban Air-Quality Estimation Using Visual Cues and a Deep Convolutional Neural Network in Bengaluru (Bangalore), India.Environmental science & technology · 2024Article
- Scalable Multipollutant Exposure Assessment Using Routine Mobile Monitoring Platforms.Research report (Health Effects Institute) · 2024Article
- Population Neuroscience: Principles and Advances.Current topics in behavioral neurosciences · 2024Review
- The leading global health challenges in the artificial intelligence era.Frontiers in public health · 2023Review
- Characterisation of urban environment and activity across space and time using street images and deep learning in Accra.Scientific reports · 2022Article
- Digital health for climate change mitigation and response: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Predicting walking-to-work using street-level imagery and deep learning in seven Canadian cities.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) is revolutionizing our world, with applications ranging from medicine to engineering.
objectivesHere we discuss the promise, challenges, and probable data sources needed to apply AI in the fields of exposure science and environmental health. In particular, we focus on the use of deep convolutional neural networks to estimate environmental exposures using images and other complementary data sources such as cell phone mobility and social media information. DISCUSSION: Characterizing the health impacts of multiple spatially-correlated exposures remains a challenge in environmental epidemiology. A shift toward integrated measures that simultaneously capture multiple aspects of the urban built environment could improve efficiency and provide important insights into how our collective environments influence population health. The widespread adoption of AI in exposure science is on the frontier. This will likely result in new ways of understanding environmental impacts on health and may allow for analyses to be efficiently scaled for broad coverage. Image-based convolutional neural networks may also offer a cost-effective means of estimating local environmental exposures in low and middle-income countries where monitoring and surveillance infrastructure is limited. However, suitable databases must first be assembled to train and evaluate these models and these novel approaches should be complemented with traditional exposure metrics.
conclusionsThe promise of deep learning in environmental health is great and will complement existing measurements for data-rich settings and could enhance the resolution and accuracy of estimates in data poor scenarios. Interdisciplinary partnerships will be needed to fully realize this potential.
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