Deep learning in magnetic resonance enterography for Crohn’s disease assessment: a systematic review

Ofir Brem, David Elisha, Eli Konen, Michal Amitai, Eyal Klang

Research output: Contribution to journalReview articlepeer-review

Abstract

Crohn’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 the role of deep learning in improving MRE for CD assessment. 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‐2 tool. Five eligible studies, encompassing 468 subjects, were identified. Our study suggests that diverse deep learning applications, including image quality enhancement, bowel segmentation for disease burden quantification, and 3D reconstruction for surgical planning 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 deep learning can impact CD patient diagnostics, particularly when considering the increasing integration of such models into hospital systems.

Original languageEnglish
JournalAbdominal Radiology
DOIs
StateAccepted/In press - 2024

Keywords

  • Convoluted neural networks
  • Crohn’s disease
  • Deep learning
  • Inflammatory bowel disease
  • MRE

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