Recurrent attentive non-invasive observation of intestinal inflammation is essential for the proper management of Crohn's disease (CD).
The goal of this study was to develop and evaluate a multi-modal machine-learning (ML) model to assess ileal CD endoscopic activity by integrating information from Magnetic Resonance Enterography (MRE) and biochemical biomarkers.
We obtained MRE, biochemical and ileocolonoscopy data from the multi-center ImageKids study database.
We developed an optimized multimodal fusion ML model to non-invasively assess terminal ileum (TI) endoscopic disease activity in CD from MRE data. We determined the most informative features for model development using a permutation feature importance technique.
The optimized fusion model performed better than the clinically recommended model determined by both a better median test MSE distribution and a better aggregated AUC over the folds.