AI can predict a city's obesity levels from its buildings

AI can estimate obesity rates by analysing satellite imagery of urban areas

Anthony Cuthbertson
Tuesday 04 September 2018 21:32 BST
Physical characteristics of a neighborhood, such as parks and high-rise buildings, can have a significant impact on its inhabitants' health
Physical characteristics of a neighborhood, such as parks and high-rise buildings, can have a significant impact on its inhabitants' health

An artificial intelligence algorithm has been trained to spot cities prone to obesity simply by analysing its buildings and infrastructure.

Researchers at the University of Washington used satellite and Street View images from Google Maps to study four urban areas in the United States: Los Angeles, California; Memphis, Tennessee; San Antonio, Texas; and Seattle, Washington.

Using neural networks, the researchers were able to discover a relationship between a location's infrastructure and its obesity prevalence.

Obesity is a major health issue in the US, with one in three adults suffering from the condition. Numerous studies have been carried out to better understand the phenomenon, with previous research linking obesity to factors including genetics, diet, physical activity and the environment.

More than one in three adults in the US suffer from obesity, with an estimated 603 million people worldwide affected by the condition

The most recent study was unique in the tools it used to examine the link between built environments and obesity levels.

"This study illustrates that convolutional neural networks can be used to automate the extraction of features of the built environment from satellite images for studying healthy indicators," wrote the authors of the study, which was published in JAMA Network Open.

"Results in the study support the association between features of the built environment and obesity prevalence."

Further research could focus on assessing disparities based on neighbourhood racial composition and socioeconomic status, which has been linked to obesity and other health issues.

"Care must be taken in not over-interpreting any results," said biostatistician Benjamin Goldstein from Duke University, in a commentary on the study. "Even so, in the same way a biomarker may serve as a useful indicator of disease risk, these neighbourhood factors can serve as a valuable indicator of health outcomes.

"Going forward, it is likely that machine learning methods will be integral to discovering features associated with disease – likely features never previously suspected."

Eventually, the research could help public health bodies and improve community planning efforts for the purpose of fighting obesity.

The study's authors say the results could be used to make structural changes to built environments prone to obesity in order to encourage physical activity and introduce other measures to promote healthy living.

The study concludes: "Neighbourhood-level interventions to encourage physical activity and increase access to healthy food outlets could be combined with individual-level interventions to aid in curbing the obesity epidemic."

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