Deep Learning-Aided Diagnosis of Autoimmune Blistering Diseases

ABSTRACT Autoimmune blistering diseases (AIBDs) are rare, chronic disorders of the skin and mucous membranes, with a broad spectrum of clinical manifestations and morphological lesions. Considering that 1) diagnosis of AIBDs is a challenging task, owing to their rarity and heterogeneous clinical features, and 2) misdiagnoses are common, and the resulting diagnostic delay is a major factor in their high mortality rate, patient prognosis stands to benefit greatly from the development of a computer-aided diagnostic (CAD) tool for AIBDs. Artificial intelligence (AI) research into rare skin diseases like AIBDs is severely underrepresented, due to a variety of factors, foremost a lack of large-scale, uniformly curated imaging data. A study by Julia S. et al. finds that, as of 2020, there exists no machine learning studies on rare skin diseases [1], despite the demonstrated success of AI in the field of dermatology. Whereas previous research has primarily looked to improve performance through extensive data collection and preprocessing, this approach remains tedious and impractical for rarer, under-documented skin diseases. This study proposes a novel approach in the development of a deep learning based diagnostic aid for AIBDs. Leveraging the visual similarities between our imaging data with pre-existing repositories, we demonstrate automated classification of AIBDs using techniques such as transfer learning and data augmentation over a convolutional neural network (CNN). A three-loop process for training is used, combining feature extraction and fine-tuning to improve performance on our classification task. Our final model retains an accuracy nearly on par with dermatologists’ diagnostic accuracy on more common skin cancers. Given the efficacy of our predictive model despite low amounts of training data, this approach holds the potential to benefit clinical diagnoses of AIBDs. Furthermore, our approach can be extrapolated to the diagnosis of other clinically similar rare diseases.