Figure from Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity, Adyasha Maharana and Elaine Okanyene Nsoesie. Image on the right represents actual obesity prevalence; on the left, cross-validated estimates of obesity prevalence based on features of the built environment extracted from satellite images. Figure reproduced under CC-BY licence. The 11th International Conference on Learning Representations (ICLR) took place last week in Kigali, Rwanda, the first time a major AI conference has taken place in-person in Africa. The program included workshops, contributed talks, affinity group events, and socials.

Obesity is a global epidemic causing at least 2.8 million deaths per year. This complex disease is associated with significant socioeconomic burden, reduced work productivity, unemployment, and other social determinants of Health (SDoH) disparities. Objective: The objective of this study was to investigate the effects of SDoH on obesity prevalence among adults in Shelby County, Tennessee, USA using a geospatial machine-learning approach. Obesity prevalence was obtained from publicly available CDC 500 cities database while SDoH indicators were extracted from the U.S. Census and USDA. We examined the geographic distributions of obesity prevalence patterns using Getis-Ord Gi* statistics and calibrated multiple models to study the association between SDoH and adult obesity. Also, unsupervised machine learning was used to conduct grouping analysis to investigate the distribution of obesity prevalence and associated SDoH indicators. Results depicted a high percentage of neighborhoods experiencing high adult obesity prevalence within Shelby County. In the census tract, median household income, as well as the percentage of individuals who were black, home renters, living below the poverty level, fifty-five years or older, unmarried, and uninsured, had a significant association with adult obesity prevalence. The grouping analysis revealed disparities in obesity prevalence amongst disadvantaged neighborhoods. More research is needed that examines linkages between geographical location, SDoH, and chronic diseases. These findings, which depict a significantly higher prevalence of obesity within disadvantaged neighborhoods, and other geospatial information can be leveraged to offer valuable insights informing health decision-making and interventions that mitigate risk factors for increasing obesity prevalence.

Scientists have created an AI that can detect obesity from space. The software scans satellite images and predicts how many residents are overweight based on the availability of parks, fast food stores and other buildings in the area. Researchers used deep learning to scan 150,000 high-resolution satellite images from Google Maps in order to identify patterns. They looked at data in six US cities - Bellevue, Seattle, Tacoma, Los Angeles, Memphis, and San Antonio. The team found that features of the built environment explained 64.8 per cent of the variation in obesity between cities.