Deep Learning in Magnetic Resonance Enterography for Crohn’s Disease Assessment: A Systematic Review

Abstract

AbstractCrohn’s disease (CD) poses significant morbidity, underscoring the need for effective, non-invasive inflammatory assessment using magnetic resonance enterography (MRE).This literature review evaluates recent publications on deep learning’s role in enhancing MRE segmentation, image quality, and visualization of inflammatory activity related to CD.We searched MEDLINE/PUBMED for studies that reported the use of deep learning algorithms for assessment of CD activity. The study was conducted according to the PRISMA guidelines. The risk of bias was evaluated using the QUADAS_J2 tool.Five eligible studies, encompassing 468 subjects, were identified.Our study suggests that diverse deep learning applications, including image quality enhancement, bowel segmentation, and motility measurement are useful and promising for CD assessment. However, most of the studies are preliminary, retrospective studies, and have a high risk of bias in at least one category.Future research is needed to assess how automated deep learning can impact patient care, especially when considering the increasing integration of these models into hospital systems.