AI-based detection of erythema migrans and disambiguation against other skin lesions.

This study examines the use of AI methods and deep learning (DL) for prescreening skin lesions and detecting the characteristic erythema migrans rash of acute Lyme disease. Accurate identification of erythema migrans allows for early diagnosis and treatment, which avoids the potential for later neurologic, rheumatologic, and cardiac complications of Lyme disease. We develop and test several deep learning models for detecting erythema migrans versus several other clinically relevant skin conditions, including cellulitis, tinea corporis, herpes zoster, erythema multiforme, lesions due to tick bites and insect bites, as well as non-pathogenic normal skin.