Automated Eosinophil Quantification Using Deep Learning to Predict Therapy Escalation in Pediatric Ulcerative Colitis.
AI interpretation is pending for this paper.
Open original publication →What the AI sees
Not AI summarized yet.
Research significance
Pending deeper interpretation.
Source abstract
INTRODUCTION: Emerging evidence implicates eosinophils as important modulators of disease activity and therapeutic response in ulcerative colitis (UC). 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 model to automate eosinophil detection, using whole-slide images from 2 pediatric sites. Pathologist-annotated 500 × 500-pixel patches served as training data; model performance was evaluated using area under the receiver operator curve (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 1-year clinical outcomes including anti-tumor necrosis factor α (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, respectively). Lower eosinophil counts predicted escalation to anti-TNFα therapy ( P = 0.04). Incorporation of eosinophil metrics into Predicting Response to Standardized Pediatric Colitis Therapy (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 to guide therapy.