Explainable AI-based analysis of human pancreas sections identifies traits of type 2 diabetes

Abstract Type 2 diabetes (T2D) is a chronic disease currently affecting around 500 million people worldwide with often severe health consequences. Yet, histopathological analyses are still inadequate to infer the glycaemic state of a person based on morphological alterations linked to impaired insulin secretion and β-cell failure in T2D. Giga-pixel microscopy can capture subtle morphological changes, but data complexity exceeds human analysis capabilities. In response, we generated a dataset of pancreas whole-slide images with multiple chromogenic and multiplex fluorescent stainings and trained deep learning models to predict the T2D status. Using explainable AI, we made the learned relationships interpretable, quantified them as biomarkers, and assessed their association with T2D. Remarkably, the highest prediction performance was achieved by simultaneously focusing on islet α-and δ-cells and neuronal axons. Subtle alterations in the pancreatic tissue of T2D donors such as smaller islets, larger adipocyte clusters, altered islet-adipocyte proximity, and fibrotic patterns were also observed. Our innovative data-driven approach underpins key findings about pancreatic tissue alterations in T2D and provides novel targets for research.