Development of a multimodal machine-learning fusion model to non-invasively assess ileal Crohn's disease endoscopic activity.

Background And Objective: 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.

Methods: We obtained MRE, biochemical and ileocolonoscopy data from the multi-center ImageKids study database.