Research Story Tip: AI and Deep Learning Can Analyze 'Rash Selfies' for Better Lyme Disease Detection

A report on the findings was published in the October 2020 issue of the journal Computers in Biology and Medicine. APL scientists developed and tested several deep learning computer models to accurately pick out EM from other dermatological conditions and normal skin. The DL models were "trained" to discern the appearance of EM using images of non-EM rashes and normal skin available in the public domain, and clinical photos of patients with EM provided by the Johns Hopkins University Lyme Disease Research Center and the Lyme Disease Biobank, part of the Johns Hopkins University School of Medicine's Division of Rheumatology. There are more than 300,000 new cases of Lyme disease annually in the United States and treatment is most effective if it is caught early. Misdiagnosis, especially in the disease's initial stages, is common because of several challenges.