A picture tells a thousand…exposures: Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology

Abstract

Background: Artificial intelligence (AI) is revolutionizing our world, with applications ranging from medicine to engineering.

Objectives: Here 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.

Conclusions: The 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.

Fig. 1. Principles of physically-based and geostatistical models.

Fig. 2. Heat maps can be used to highlight areas of images used to make predictions (simulated example).

Fig. 3. Deep convolutional neural networks can have multiple inputs and multiple outputs. The top panel illustrates a model developed using local images, satellite images, and audio data. Pre-trained models can be applied to new images to make predictions for multiple exposures (bottom panel).