AI-Based Study Shows Link Between Environment, Cardiovascular Outcomes

Using a machine learning-powered analysis of Google Street View, a study found that people who live in an environment rich in sidewalks, trees, and clear sky have a significantly lower risk of a major adverse cardiac event (MACE). The findings were presented at the American College of Cardiology 73rd Annual Scientific Session & Expo.

This study is part of ongoing research assessing how greenspace and environmental influences impact cardiovascular risk. Previous studies, which have used a variety of approaches to quantify environmental features, have had mixed results. In this analysis, researchers used Google Street View images with robust segmentation methods to analyze “vertical greenspace”—the view along the skyline and not only on the ground—to provide a more granular view of individual streets rather than averaging across entire counties or ZIP codes. The study consisted of nearly 50,000 participants who were part of a program that provides free and low-cost coronary artery calcium tests to people in northeastern Ohio.

The study showed that over an average follow-up period of approximately 27 months, around 2000 participants had experienced a MACE, such as heart attack, stroke, or death from heart disease. By analyzing participants’ built environment, researchers found that people living in areas with more sidewalks were 9% less likely to suffer major cardiac events than people living in places with fewer or no sidewalks. Moreover, the researchers observed that people living in places that scored high for vertical greenspace—trees and clear sky—were 5% less likely to suffer these events than people in areas that scored low for this metric. These associations were independent of each other.

“A lot of research has shown that environmental factors strongly affect our health. If we can find a way to stratify this risk and provide interventions before cardiovascular events happen, then we could save a lot of lives,” said Zhuo Chen, PhD, a postdoctoral researcher at Case Western Reserve University and University Hospitals Health System in Cleveland and the study’s lead author, via a press release. “Our study shows that with advanced computer vision algorithms and [artificial intelligence [AI]), we now have the ability to quantify the built environment more effectively and efficiently. If we can assess the individual’s risk at a granular level, we could provide more personalized interventions.”