Image-based machine learning algorithms for disease characterization in the human type 1 diabetes pancreas

Emerging data suggest that type 1 diabetes affects not only the β-cell-containing islets of Langerhans, but also the surrounding exocrine compartment. Using digital pathology, machine learning algorithms were applied to provide high-resolution, whole-slide images of human pancreata to determine if the tissue composition in individuals with or at-risk for type 1 diabetes differs from those without diabetes. Transplant grade pancreata from organ donors were evaluated from 16 non-diabetic autoantibody negative controls, 8 non-diabetic autoantibody positive subjects who have increased-type 1 diabetes risk, and 19 persons with type 1 diabetes (0-12 years duration). HALO image analysis algorithms were implemented to compare architecture of the main pancreatic duct as well as cell size, density, and area of acinar, endocrine, ductal, and other non-endocrine, non-exocrine tissues. Type 1 diabetes was found to affect exocrine area, acinar cell density, and size while the type of difference correlated with the presence or absence of insulin-positive cells remaining in the pancreas. These changes were not observed before disease onset, as indicated by modeling cross-sectional data from pancreata of autoantibody positive subjects and those diagnosed with type 1 diabetes. These data provide novel insights into anatomical differences in type 1 diabetes pancreata and demonstrate that machine learning can be adapted for the evaluation of disease processes from cross-sectional datasets.