Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis

Background: Emerging evidence implicates eosinophils as important modulators of disease activity and therapeutic response in ulcerative colitis. Automated image analysis provides a scalable and reproducible approach to their evaluation, overcoming the limitations of manual quantification. Methods: We developed a U-Net-based deep learning (DL) model to automate eosinophil detection, using whole-slide images from two pediatric sites. Pathologist-annotated 500×500-pixel patches served as training data, model performance was evaluated using AUROC, precision, recall, and F1 score and benchmarked against SAU-Net, YOLO-v8, and HoVer-Next. The validated algorithm was applied to rectal biopsies from treatment-naïve children with moderate to severe UC in the PROTECT inception cohort (n=221). Eosinophil metrics (density in cells/mm 2 and peak high-power field counts) were analyzed for associations with baseline clinical, histologic and endoscopic features, and one-year clinical outcomes including anti-TNFα escalation and corticosteroid-free clinical remission. Predictive models were evaluated with logistic regression and machine learning approaches using 5-fold cross-validation. Results: The segmentation model achieved high accuracy (test AUROC=0.94, F1 score=0.86) and strong concordance with expert counts (ρ=0.87–0.89). Higher eosinophil density and counts correlated with basal lymphoid aggregates (p<0.001) and predicted week-4 and corticosteroid-free remission (p=0.03 and p=0.01). Lower eosinophil counts predicted escalation to anti-TNFα therapy (p=0.04). Incorporation of eosinophil metrics into PROTECT clinical models maintained predictive accuracy for anti-TNFα escalation (AUROC=0.79; accuracy=0.76). Discussion: Deep learning–based eosinophil quantification has the potential to transform pediatric UC care by serving as a reproducible biomarker that informs risk stratification and guide therapy.