A modular pancreas segmentation and multimodal fusion framework for clinical and CT decision support in acute pancreatitis

Timely decision support in emergency imaging requires robust integration of structured clinical variables and contrast-enhanced CT while minimizing reader-dependent processing and improving reproducibility. We propose a modular segmentation-to-fusion framework for multimodal clinical–CT prediction using acute pancreatitis as a case study. The pipeline uses automated pancreas ROI generation with post-segmentation quality control, extracts complementary radiomics and frozen deep features, and integrates multimodal evidence through a calibrated stacked ensemble. We evaluated the framework on a retrospective single-center contrast-enhanced CT cohort using internal training and held-out test splits, assessing discrimination, calibration, and decision-curve performance. The proposed multimodal stacked ensemble achieved an AUC of 0.913 (95% CI 0.840–0.972) on the internal held-out test cohort and showed improved calibration and decision-curve performance compared with comparator models. These results suggest that reproducible multimodal clinical-imaging fusion may provide complementary information for internal risk estimation of AP in a single-center retrospective setting.