Streamlining the Histopathological Workflow in Diabetic Kidney Disease with Artificial Intelligence

Background: Assessment of pathology endpoints in animal models of diabetic kidney disease (DKD) is time-consuming and prone to expert bias. Additionally, the sparsity of human kidney biopsy data hinders the development of translational models from animals to humans. Methods: We developed an AI-driven workflow to streamline histopathological assessments in animal models of diabetic nephropathy. Our approach (i) detected glomeruli in whole slide images, (ii) enabled fast expert scoring via an annotation tool, and (iii) automated scoring. By leveraging unlabeled preclinical data for self-supervised learning, we enhanced AI scoring performance, reduced expert bias, and enabled the translation of AI scoring from animal models to human biopsies. To translate AI models from preclinical studies to human biopsies, we introduced a method that adjusted the feature extractor to human-specific features during inference without the need for annotated examples. Results: Our annotation tool streamlined glomerular scoring, reducing turnaround time by 80%. Supervised AI models outperformed expert agreement and further reduced turnaround time by 90%, demonstrating generalization across studies involving both the same and different animal models. Without supervision, the self-supervised model achieved a κ value of 0.78, effectively identifying glomerular changes without guidance. Incorporating self-supervised learning into supervised training improved performance to κ = 0.84 and reduced bias compared to individual experts (P < 0.001). Our translational approach achieved a κ value of 0.63 on human glomeruli, even though the model was trained exclusively on mouse glomeruli scores, reducing the translational gap by 45%. Conclusions: In this study, we accelerated and enhanced pathology readouts in a real-life pharmaceutical industry setting. We show that AI-assisted scoring reduced pathologists' workload and expedited study assessments. Self-supervised learning captured intrinsic properties of kidney morphology without expert annotation, reduced expert bias and translational discrepancies, greatly facilitating translational activities in drug development for patients with DKD.