Deep learning for giant cell arteritis diagnosis on temporal artery biopsy.

Giant Cell Arteritis (GCA) is a vasculitis affecting large and medium-caliber arteries, requiring early and accurate diagnosis to prevent serious complications. Temporal artery biopsy (TAB) is the gold standard for histopathological diagnosis, but its evaluation is challenging, time-consuming, and requires significant expertise. This study aimed to assess the accuracy of a deep learning model in diagnosing GCA from TAB images. We assembled a training cohort of 366 patients (137 GCA, 229 controls) and an external testing cohort of 58 patients (21 GCA, 37 controls). All whole-slide images (WSI) of hematoxylin-eosin-saffron-stained sections were digitized. Using CTransPath for feature representation, we trained a deep learning model with an {Updating}attention-based multiple-instance learning mechanism. The attention scores were analyzed to provide insights into the model's decision-making process. The models achieved a mean area under the receiver operating characteristic curve (AUROC) of 0.987 (±0.018) in 5-fold cross-validation on the training cohort. With a single misclassified case, they achieved a mean AUROC of 0.994 (±0.001) on the external testing cohort. The attention mechanism identified inflammatory infiltrates in the media layer as the primary features influencing predictions, demonstrating the model's interpretability. Our deep learning framework achieved state-of-the-art performance in GCA diagnosis from TAB images. By accurately identifying critical features, it has the potential to assist pathologists in prescreening, reducing workload and improving diagnostic consistency.