Google Street View could be used to predict the risk of coronary heart disease by analyzing the neighborhood environment, according to a study.

Research published in european heart journalWe monitored our daily environment, including the quality of roads, buildings, and green spaces, and the role they play in determining heart health.

Researchers have been able to track the link between environmental factors, such as the presence or absence of pollution, and the risk of coronary heart disease (a condition in which the heart’s arteries become clogged, restricting blood flow to the heart). Ta.

They found that these neighborhood characteristics may explain 63% of the differences in heart disease risk by region.

The findings come from an analysis of more than 500,000 Google Street View images across U.S. cities using computer vision to identify and interpret details that may go unnoticed by the human eye. Was born.

This data provides urban planners and public health officials with information to design healthier living spaces and neighborhoods.

“There is definitely a huge amount of demographic, social, economic and environmental datasets out there,” said study author Sanjay Rajagopalan of University Hospital Harrington Heart and Vascular Institute and Case Western Reserve University in Ohio. the professor said.

“However, significant gaps remain in our knowledge about the unique environmental aspects of the data that are currently beyond human recognition and interpretation.

“Here, using computer vision approaches has the potential to enable an unparalleled understanding of the physical and built environment.”

The study also included images from Detroit, Michigan. Kansas City, Missouri. Cleveland, Ohio. Brownsville, Texas. Fremont, California. Bellevue, Washington and Denver, Colorado.

By leveraging artificial intelligence, researchers can now examine the details of a neighborhood’s built environment.

The research leverages computer vision technology, including applications used in medical imaging and autonomous driving, to identify and improve details, similar to estimating disease risk and navigating vehicles safely. Interpreted and made predictions and decisions.

“We are reaching a point in human civilization where the use of artificial intelligence to enhance human understanding will be critical to solving complex problems,” Professor Rajagopalan said. .

“Traditional approaches that have been used to date are quite limited, as they often rely on linear interpretation of existing, highly curated, one-dimensional datasets by humans.”

Updated: March 28, 2024, 12:05 AM