AI-Assisted MRE for Intestinal Fibrosis in Crohn's Disease
A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease (CD) Using MR Enterography (MRE)-Based Artificial Intelligence
Minhu Chen
234 participants
Jun 3, 2025
OBSERVATIONAL
Conditions
Summary
Intestinal fibrotic strictures represent a severe complication of Crohn's disease (CD), affecting over half of the patients. Despite the continuous emergence of novel medications, effective treatment options remain scarce. Endoscopy fails to identify the full-thickness fibrosis of the bowel wall, and standardized assessment for cross-sectional imaging has yet to be established. Previous studies have demonstrated that radiomics models based on computed tomography and deep learning models exhibit commendable diagnostic capability. Thus, this project seeks to conduct a prospective multicenter study, with plans to recruit 234 CD patients requiring bowel resection from five medical centers. The aim is to develop and validate a deep learning model based on magnetic resonance enterography (MRE) to accurately characterize intestinal fibrosis.
Eligibility
Inclusion Criteria3
- Patients Over 18 years old with a confirmed diagnosis of CD based on the criteria of ECCO guideline.
- Planning to receive a bowel resection due to stricture in ileum or colon, and availability of histological specimens of resected intestinal walls matched with MRE are expected to be available.
- Clear boundaries of the target bowel tract enable accurate semi-automatic or fully automatic intestinal segmentation
Exclusion Criteria5
- Cannot undergo MRI examination
- Difficult to obtain suitable tissue after surgery
- MRE imaging is of poor quality or contains artifacts
- The target bowel is located at the anastomosis (ie, anastomotic stricture)
- Intestinal lesions concurrent with other diseases
Locations(5)
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NCT06858553