IBDAIM:Artificial intelligence for analyzing intestinal biopsies pathological images for assisted integrated diagnostic of inflammatory bowel disease.

BACKGROUND

Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is challenging to diagnose accurately from pathological images due to its complex histological features. This study aims to develop an artificial intelligence (AI) model, IBDAIM, to assist pathologists in quickly and accurately diagnosing IBD by analyzing whole-slide images (WSIs) of intestinal biopsies.

METHODS

This retrospective cohort study used data from two institutions, Nanjing Drum Tower Hospital (NDTH) and Zhujiang Hospital (ZJH). The NDTH dataset was randomly divided into a model development set and an internal test set, while the ZJH dataset served as an external validation set. We developed a weakly supervised deep learning model, IBDAIM, that uses WSI-level diagnostic labels without detailed annotation. The model integrates features from patch-level predictions using Patch Likelihood Histogram (PLH) and Bag of Words (BoW) to build WSI-level representations. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), accuracy (ACC), sensitivity, and specificity. Probability plots and heatmaps were generated to analyze and visualize the diagnostic labels and organizational results of WSIs. Additionally, the model was applied to assist pathologists in diagnosis, and the improvement in diagnostic performance was assessed.

RESULTS

In the normal intestinal mucosa vs. IBD task, the internal test cohort achieved an AUROC of 0.998 (95% CI 0.995-1.000) and ACC of 0.982, while the external test cohorts achieved an AUROC of 0.967 (95% CI 0.939-0.995) and ACC of 0.934. For the CD vs. UC task, the internal test cohort achieved an AUROC of 0.972 (95% CI 0.942-1.000) and ACC of 0.901, and the external test cohorts achieved an AUROC of 0.952 (95% CI 0.923-0.982) and ACC of 0.949. The model's performance exceeded that of five pathologists, and AI assistance significantly improved diagnostic accuracy across all pathologists.

CONCLUSION

The IBDAIM model demonstrates high performance in diagnosing IBD biopsy pathological images and can effectively assist pathologists in identifying normal intestinal mucosa, CD, and UC tissues. This AI tool enhances diagnostic efficiency and accuracy, supporting better clinical decision-making and patient outcomes.