BlotDx: A deep learning tool for Western blot-based diagnostics

Background Western blot (WB) is the gold standard for herpes simplex virus (HSV-1/HSV-2) serology but requires manual interpretation by trained laboratory personnel, which is time-consuming and variable. Automating this process could improve efficiency and diagnostic consistency. This study presents BlotDx, a deep learning tool for automated interpretation of HSV WB assays. Methods BlotDx uses a two-stage approach: (1) instance segmentation or object detection to identify blot strips from input images, and (2) classification models to determine HSV-1 and HSV-2 serostatus. The dataset used contained 34 images, each with up to 24 pairs of blot strips. Of these, 21 images were used for training, 4 for validation, and 9 for testing (n = 314, 57, and 124 blot pairs respectively). Data augmentation, ensemble modeling, and transfer learning were used to enhance model performance. Findings Compared to the gold standard of expert human review by three independent MLSs, BlotDx demonstrated high diagnostic accuracy (99% for HSV-1, 98% for HSV-2) across validation and test datasets. Data augmentation and ensemble methods significantly improved robustness and reliability. The transfer learning approach allowed the models to leverage pre-trained weights, further enhancing performance. Interpretation This study highlights the utility of AI in automating the analysis of Western blots for HSV diagnostics, with potential applications in other diseases. The proposed two-stage approach, combined with modern deep learning techniques, achieved scalable, accurate, and efficient diagnostic results. These findings underscore the potential of deep learning to transform traditional diagnostic workflows by reducing costs and increasing efficiency.