Deep learning models capture histological disease activity in Crohn's Disease and Ulcerative Colitis with high fidelity.

BACKGROUND AND AIMS: Histologic disease activity in Inflammatory Bowel Disease (IBD) is associated with clinical outcomes and is an important endpoint in drug development. We developed deep learning models for automating histological assessments in IBD. METHODS: Histology images of intestinal mucosa from phase 2 and phase 3 clinical trials in Crohn's disease (CD) and Ulcerative Colitis (UC) were used to train artificial intelligence (AI) models to predict the Global Histology Activity Score (GHAS) for CD and Geboes histopathology score for UC. Three AI methods were compared. AI models were evaluated on held-back testing sets and model predictions were compared against an expert central reader and five independent pathologists. RESULTS: The model based on multiple instance learning and the attention mechanism (SA-AbMILP) demonstrated the best performance among competing models. AI modeled GHAS and Geboes sub-grades matched central readings with moderate to substantial agreement, with accuracies ranging from 65% to 89%. Furthermore, the model was able to distinguish the presence and absence of pathology across four selected histological features with accuracies for colon, in both CD and UC, ranging from 87% to 94% and, for CD ileum, ranging from 76% to 83%. For both CD and UC, and across anatomical compartments (ileum and colon) in CD, comparable accuracies against central readings were found between the model assigned scores and scores by an independent set of pathologists. CONCLUSIONS: Deep learning models based upon GHAS and Geboes scoring systems were effective at distinguishing between the presence and absence of IBD microscopic disease activity.

Comparison of Supervised and Self-Supervised Deep Representations Trained on Histological Images.

Self-supervised methods gain more and more attention, especially in the medical domain, where the number of labeled data is limited. They provide results on par or superior to their fully supervised competitors, yet the difference between information coded by both methods is unclear. This work introduces a novel comparison framework for explaining differences between supervised and self-supervised models using visual characteristics important to the human perceptual system. We apply this framework to models trained for Gleason score and conclude that self-supervised methods are more biased toward contrast and texture transformation than their supervised counterparts. At the same time, supervised methods code more information about the shape.