Home Health News Artificial intelligence can estimate an area’s obesity by analyzing its buildings — Quartz

Artificial intelligence can estimate an area’s obesity by analyzing its buildings — Quartz

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Two researchers from the University of Washington have discovered a strategy to estimate a US metropolis’s obesity degree with out having to take a look at its inhabitants.

The duo skilled an synthetic intelligence algorithm to search out the connection between a metropolis’s infrastructure and obesity ranges utilizing satellite tv for pc and Street View photos from Google. By understanding how metropolis planning influences obesity, health campaigns and new building can be coordinated to enhance a metropolis’s health, the researchers wrote in a paper published in JAMA Network Open.

The algorithm was skilled utilizing greater than 150,000 satellite tv for pc photos of six cities, in addition to 96 classes of factors of curiosity like grocery shops and pet retailers. It was then correlated with obesity charges reported from every metropolis. The researchers included the factors of curiosity as a result of they may have an impact on the exercise of a neighborhood. An space with pet retailers might have extra individuals taking canine for a stroll, as an example.

Unsurprisingly, the algorithm correlated areas with extra inexperienced areas to stroll and extra spacing between buildings with decrease obesity charges. This is indicative of wealthier neighborhoods having fewer overweight residents. The researchers acknowledged that the entangled relationship between earnings and health might skew the algorithm. However, by additional validation exams, the workforce discovered the algorithm really discovered a link between the variety of buildings and inexperienced house and obesity, not simply wealth.

The research was singularly based mostly on US knowledge, so the algorithm is unlikely to work in different nations with out changes. Different approaches to metropolis planning, and kind of energetic cultures will surely be misunderstood by the algorithm, as has been proven with bias in so many different purposes of synthetic intelligence.

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