Researchers used Google Street View to understand hundreds of elements of the built environment, such as buildings, green spaces, sidewalks, and roads, and how these elements relate to each other and the people who live in these areas. We studied how it affects coronary artery disease.

Their findings were published in the European Heart Journal [1] Today, we show that these factors can predict 63% of the variation in coronary heart disease risk from one region to another.

Coronary heart disease, in which a buildup of fatty substances in the coronary arteries cuts off the blood supply to the heart, is one of the most common forms of cardiovascular disease.

Researchers believe that using Google Street View can help provide an overview of physical environmental risk factors in the built and natural environments, helping to not only understand the risk factors in these environments, but ultimately and could help build or adapt cities to be healthier. A place to live.

The study was led by Professors Sadeer Alkindi and Sanjay Rajagopalan of the Harrington Heart and Vascular Institute and Case Western Reserve University Hospital in Ohio, USA, and Dr. Zhuo Chen, a postdoctoral fellow in Professor Rajagopalan’s lab. .

Professor Rajagopalan said: “We’ve always been interested in how the environment, both built and natural, impacts cardiovascular disease. Here in America, zip code is more important than a measure of personal health. is also said to be good at predicting heart disease. However, investigating how the environment affects large populations in multiple cities is not an easy task. We used a machine vision-based approach to assess the association between the built environment and the prevalence of coronary heart disease in U.S. cities.”

The study included more than 500,000 Google Street View images of Detroit, Michigan. Kansas City, Missouri. Cleveland, Ohio. Brownsville, Texas. Fremont, California. Bellevue, Washington. and Denver, Colorado. The researchers also collected data on the prevalence of coronary heart disease based on “census tracts.” These are areas smaller than a U.S. zip code and home to an average of 4,000 people. The researchers used an approach called convolutional neural networks. A type of artificial intelligence that can recognize and analyze patterns in images to make predictions.

The study found that features of the built environment displayed in Google Street View images could predict 63% of the variation in coronary heart disease between these small areas in U.S. cities.

Professor Al Kindi added: “Also, an approach called attention mapping that highlights some of the important regions within an image provides a semi-qualitative interpretation of some of the thousands of features considered important in coronary heart disease. For example, features such as green space and walkable roads were associated with lower risk, whereas other features such as unpaved roads were associated with higher risk. , these findings require further investigation.

“We show that computer vision approaches can be used to identify environmental factors that influence cardiovascular risk, which could play a role in guiding heart-healthy urban planning. The fact that we can do this at scale is extremely unique and important for urban planning.”

“Given future challenges such as climate change and demographic change, and with nearly 70% of the world’s population living in urban environments, we are using computer vision approaches that can provide minute details instantly to improve urban environments. There is a dire need to understand this at scale, at an unparalleled level,” Professor Rajagopalan said.

In the accompanying editorial, [2] Dr Rohan Khera from Yale University School of Medicine said: This is often summarized by the expression, “Your zip code is a bigger determinant of your health than your genetic information.” However, our ability to properly classify environmental risk factors relied on population surveys that track wealth, pollution, and local resources.

“The study by Chen et al. presents a novel and more comprehensive assessment of the built environment. This work creatively leverages Google’s panoramic street view imagery to complement Google’s widely used mapping application. I am making use of it.

“…an AI-powered approach to studying the association between the physical environment and cardiovascular health shows that across our communities, measures of cardiovascular health are strongly encoded simply in the appearance of neighborhoods.” It also highlighted that efforts to improve cardiovascular health in communities with the greatest need include defining strategic priorities to identify vulnerable communities. It is important to use this information wisely as we double down on this.”

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