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. 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.

Automated detection of erythema migrans and other confounding skin lesions via deep learning.

Lyme disease can lead to neurological, cardiac, and rheumatologic complications when untreated. Timely recognition of the erythema migrans rash of acute Lyme disease by patients and clinicians is crucial to early diagnosis and treatment. Our objective in this study was to develop deep learning approaches using deep convolutional neural networks for detecting acute Lyme disease from erythema migrans images of varying quality and acquisition conditions. This study used a cross-sectional dataset of images to train a model employing a deep convolutional neural network to perform classification of erythema migrans versus other skin conditions including tinea corporis and herpes zoster, and normal, non-pathogenic skin. Evaluation of the machine's ability to classify skin types was also performed on a validation set of images. Machine performance for detecting erythema migrans was further tested against a panel of non-medical humans. Online, publicly available images of both erythema migrans and non-Lyme confounding skin lesions were mined, and combined with erythema migrans images from an ongoing, longitudinal study of participants with acute Lyme disease enrolled in 2016 and 2017 who were recruited from primary and urgent care centers. The final dataset had 1834 images, including 1718 expert clinician-curated online images from unknown individuals with erythema migrans, tinea corporis, herpes zoster, and normal skin. It also included 116 images taken of 63 research participants from the Mid-Atlantic region. Two clinicians carefully annotated all lesion images. A convenience sample of 7 non-medically-trained humans were used as a panel to compare against machine performance. We calculated several performance metrics, including accuracy and Kappa (characterizing agreement with gold standard), as well as a receiver operating characteristic curve and associated area under the curve. For detecting erythema migrans, the machine had an accuracy (95% confidence interval error margin) of 86.53% (2.70), ROCAUC of 0.9510 (0.0171) and Kappa of 0.7143. Our results suggested substantial agreement between machine and clinician criterion standard. Comparison of machine with non-medical expert human performance indicated that the machine almost always exceeded acceptable specificity, and could operate with higher sensitivity. This could have benefits for prescreening prior to physician referral, earlier treatment, and reductions in morbidity.