2021
DOI: 10.1101/2021.08.29.21262424
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Development of a multimodal machine-learning fusion model to non-invasively assess ileal Crohn’s disease endoscopic activity

Abstract: Background and Aims: Endoscopic healing (EH), is a major treatment goal for Crohn's disease(CD). However, terminal ileum (TI) intubation failure is common, especially in children. We evaluated the added-value of machine-learning models in imputing a TI Simple Endoscopic Score for CD (SES-CD) from Magnetic Resonance Enterography (MRE) data of pediatric CD patients. Methods: This is a sub-study of the prospective ImageKids study. We developed machine-learning and baseline linear-regression models to predict TI S… Show more

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