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

• Published in: Computers in biology and medicine 2020-10, Vol.125, p.103977-103977, Article 103977
• Main Authors: Burlina, Philippe M., Joshi, Neil J., Mathew, Phil A., Paul, William, Rebman, Alison W., Aucott, John N.
• Format: Article
• Language: English
• Subjects: Antibiotics; Artificial intelligence; Bacterial infections; Cellulitis; Classification; Complications; Coronary artery disease; Deep learning; Dermatology; Diagnosis; Erythema; Erythema multiforme; Health services; Heart diseases; Herpes zoster; Infections; Insect bites; Insects; Lesions; Lyme disease; Machine learning; Medical imaging; Melanoma; Morbidity; Neural networks; Neurological complications; Performance measurement; Physicians; Skin cancer; Skin diseases; Skin lesions; Tinea corporis; Vector-borne diseases
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2020.103977

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. We consider a set of clinically-relevant binary and multiclass classification problems of increasing complexity. We train the DL models on a combination of publicly available images and test on public images as well as images obtained in the clinical setting. We report performance metrics that measure agreement with a gold standard, as well as a receiver operating characteristic curve and associated area under the curve. On public images, we find that the DL system has an accuracy ranging from 71.58% (and 95% error margin equal to 3.77%) for an 8-class problem of EM versus 7 other classes including other skin pathologies, insect bites and normal skin, to 94.23% (3.66%) for a binary problem of EM vs. non-pathological skin. On clinical images of affected individuals, the DL system has a sensitivity of 88.55% (2.39%). These results suggest that a DL system can help in prescreening and referring individuals to physicians for earlier diagnosis and treatment, in the presence of clinically relevant confusers, thereby reducing further complications and morbidity. •We examine the use of AI methods for detecting erythema migrans (EM) against the most clinically relevant skin conditions that may be “confusers.”•Early detection of EM, and diagnosis and treatment of Lyme disease, avoids potential neurologic, rheumatologic, and cardiac complications.•We develop the most extensively curated dataset thus far for this challenging problem.•We evaluate several deep learning models against various problems of growing complexity and on public domain and clinical images.•Results suggest that AI can help in prescreening and referring individuals to physicians for earlier diagnosis and treatment.