Deep Learning for Classification of Inflammatory Bowel Disease Activity in Whole Slide Images of Colonic Histopathology.
Journal:
The American journal of pathology
PMID:
39800054
Abstract
Grading activity of inflammatory bowel disease (IBD) using standardized histopathological scoring systems remains challenging due to limited availability of pathologists with IBD expertise and interobserver variability. In this study, a deep learning model was developed to classify activity grades in hematoxylin and eosin-stained whole slide images (WSIs) from patients with IBD, offering a robust approach for general pathologists. This study utilized 2077 WSIs from 636 patients who visited Dartmouth-Hitchcock Medical Center in 2018 and 2019, scanned at ×40 magnification (0.25 μm/pixel). Board-certified gastrointestinal pathologists categorized the WSIs into four activity classes: inactive, mildly active, moderately active, and severely active. A transformer-based model was developed and validated using five-fold cross-validation to classify IBD activity. Using HoVer-Net, neutrophil distribution across activity grades was examined. Attention maps from the model highlighted areas contributing to its prediction. The model classified IBD activity with weighted averages of 0.871 (95% CI, 0.860-0.883) for the area under the curve, 0.695 (95% CI, 0.674-0.715) for precision, 0.697 (95% CI, 0.678-0.716) for recall, and 0.695 (95% CI, 0.674-0.714) for F1 score. Neutrophil distribution was significantly different across activity classes. Qualitative evaluation of attention maps by a gastrointestinal pathologist suggested their potential for improved interpretability. The model demonstrates robust diagnostic performance and could enhance consistency and efficiency in IBD activity assessment.