After its convolutional neural network was trained on more than 3,000 biopsy images from patients with environmental enteropathy, celiac disease and no disease, the machine learning algorithm was able to detect the presence of the two gut diseases in imaging data with 93.4 percent accuracy. According to the study’s authors, based at the University of Virginia in Charlottesville, the algorithm is similar to that of Google’s facial recognition technology.
“If we can use these cutting-edge technologies and ways of looking at data through data science, we can get answers faster and help these children sooner,” said Sana Syed, MD, assistant professor of pediatrics at the UVA School of Medicine. Environmental enteropathy affects approximately 20 percent of children aged 5 and younger in countries with limited access to food, water and sanitation, as well as some children in rural Virginia.
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