ABSTRACT
BACKGROUND: Artificial intelligence (AI) is reshaping healthcare, using machine and deep learning (DL) to enhance disease management. Dermatology has seen improved diagnostics, particularly in skin cancer detection, through the integration of AI. However, the potential of AI in automating immunofluorescence imaging for autoimmune bullous skin diseases (AIBDs) remains untapped. While direct immunofluorescence (DIF) supports diagnosis, its manual interpretation can hinder efficiency. The use of DL to classify DIF patterns automatically, including the intercellular (ICP) and linear pattern (LP), holds promise for improving the diagnosis of AIBDs. OBJECTIVES: To develop AI algorithms for automated classification of AIBD DIF patterns, such as ICP and LP, in order to enhance diagnostic accuracy, streamline disease management and improve patient outcomes through DL-driven immunofluorescence interpretation. METHODS: We collected immunofluorescence images from skin biopsies of patients suspected of having an AIBD between January 2022 and January 2024. Skin tissue was obtained via a 5-mm punch biopsy, prepared for DIF. Experienced dermatologists classified the images as ICP, LP or negative. To evaluate our DL approach, we divided the images into training (n = 436) and test sets (n = 93). We employed transfer learning with pretrained deep neural networks and conducted fivefold cross-validation to assess model performance. Our dataset's class imbalance was addressed using weighted loss and data augmentation strategies. The models were trained for 50 epochs using Pytorch, achieving an image size of 224 × 224 pixels for both convolutional neural networks (CNNs) and the Swin Transformer. RESULTS: Our study compared six CNNs and the Swin Transformer for AIBD image classification, with the Swin Transformer achieving the highest average validation accuracy (98.5%). On a separate test set, the best model attained an accuracy of 94.6%, demonstrating 95.3% sensitivity and 97.5% specificity across AIBD classes. Visualization with Grad-CAM (class activation mapping) highlighted the model's reliance on characteristic patterns for accurate classification. CONCLUSIONS: The study highlighted the accuracy of CNNs in identifying DIF features. This approach aids automated analysis and reporting, offering reproducibility, speed, data handling and cost-efficiency. Integrating DL into skin immunofluorescence promises precise diagnostics and streamlined reporting in this branch of dermatology.
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.