A deep learning approach to direct immunofluorescence pattern recognition in autoimmune bullous diseases.

Artificial intelligence (AI) is transforming healthcare through machine and deep learning (computer systems that can learn and adapt, and make complex decisions, without receiving explicit instructions), improving disease management in dermatology, particularly in detecting skin cancer. However, AI's potential in automating immunofluorescence imaging in autoimmune bullous (blistering) skin diseases (AIBDs) remains largely untapped. Manual interpretation of direct immunofluorescence (DIF ­ a type of microscopy) can reduce efficiency. However, using deep learning to automatically classify DIF patterns (for example, the 'intercellular pattern' (ICP) and the 'linear pattern' (LP)) holds promise in helping with the diagnosis of AIBDs. This study aimed to develop AI algorithms for the automated classification of AIBD DIF patterns, such as ICP and LP, to improve diagnostic accuracy and streamline disease management. Immunofluorescence images were collected from skin biopsies of patients with a suspected AIBD between January 2022 and January 2024. Dermatologists classified the images into three categories: ICP, LP and negative. The dataset was divided into training (436 images) and test sets (93 images). A transfer learning framework (where what has been learned previously in one setting is used to improve performance in another) was used to make up for the limited amount of training data, to explore different models for the AIBD classification task. Our results revealed that a model called the 'Swin Transformer' achieved an average accuracy of 99% in diagnosing different AIBDs. The best model attained 95% accuracy on the test set and was reliable in identifying and ruling out different AIBDs. Visualization with Grad-CAM (a technique used in deep learning) highlighted the model's use of characteristic patterns to classify the diseases accurately. Overall, integrating deep learning in skin immunofluorescence promises to improve diagnostics and streamline reporting in dermatology, which could improve consistency, speed and cost-efficiency.