Abstract
Computational analysis of histopathological specimens holds promise in identifying biomarkers, elucidating disease mechanisms, and streamlining clinical diagnosis. However, the application of deep learning techniques in vascular pathology remains underexplored. Here, we present a comprehensive evaluation of deep learning-based approaches to analyze digital whole-slide images of abdominal aortic aneurysm samples from 369 patients from three European centers. Deep learning demonstrated robust performance in predicting inflammatory characteristics, particularly in the adventitia, as well as fibrosis grade and remaining elastic fibers in the tunica media from Hematoxylin and Eosin (HE)-stained slides (mean AUC > 0.70 in two external test cohorts). Models trained on Elastica van Gieson (EvG)-stained slides overall performed similar to models trained on HE-stained WSI for detection of calcification and fibrosis. For prediction of inflammatory parameters, HE-trained models performed considerably superior to EvG-trained models. Overall, this study represents the first comprehensive evaluation of computational pathology in vascular disease and has the potential to contribute to improved understanding of abdominal aortic aneurysm pathophysiology and personalization of treatment strategies, particularly when integrated with radiological phenotypes and clinical outcomes.