Deep Learning-Based Prediction of Epithelial Cytokine Responses for the Selection of Functionally Consistent Airway Organoids

Although airway organoids provide a physiologically relevant platform for modeling human airway inflammation, their utility is often limited by substantial heterogeneity in epithelial differentiation and functional responsiveness across Matrigel domes. Here, we present a non-destructive, imaging-guided framework to predict epithelial cytokine responses and enable the selection of functionally consistent airway organoid domes. Mature human airway organoids were stimulated with house dust mite (HDM) extract and dome-level inflammatory responsiveness was quantified by RT-qPCR for thymic stromal lymphopoietin (TSLP) and interleukin-33 (IL-33). Both cytokines exhibited wide dome-to-dome variability and showed a significant positive correlation, indicating coordinated allergic inflammatory regulation. Meanwhile, bright-field dome images were analyzed to segment individual organoids, define robust regions of interest, and extract quantitative morphological and texture descriptors based on gray-level co-occurrence matrix features. Organoid-level descriptors were aggregated into a single dome-level feature vector using distributional statistics, thereby capturing both central tendency and heterogeneity within each dome. Using these engineered dome-level features, we trained a deep tabular learning model (TabNet) to classify qPCR-defined inflammatory responsiveness. The resulting model achieved strong and consistent cross-validated performance for both targets, reaching balanced accuracies of 0.910 for TSLP and 0.833 for IL-33, demonstrating that bright-field phenotypes contain predictive signatures of cytokine activation. This approach provides a scalable enrichment strategy for robustly responsive organoid–Matrigel domes without destructive assay. It improves reproducibility in organoid-based airway inflammation studies and supports standardized dome selection for downstream mechanistic and translational applications.