AI-enhanced MRE to detect bowel fibrosis in Crohn's disease
Official title: A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease (CD) Using MR Enterography (MRE)-Based Artificial Intelligence
Run by First Affiliated Hospital, Sun Yat-Sen University · NCT06858553 · Data updated 2026-11-12
This project will test whether an AI program applied to magnetic resonance enterography (MRE) scans can accurately identify and characterize bowel wall fibrosis in adults with Crohn's disease who are scheduled for bowel resection.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years to 75 Years |
| Sex | All |
| Enrollment | 234 (estimated) |
| Sponsor | First Affiliated Hospital, Sun Yat-Sen University Academic / other |
| Locations | 5 sites (Guangzhou, Guangdong and 4 other locations) |
| Status | Recruiting |
| Started | June 3, 2025 |
| Main results expected | February 28, 2027 |
| Study ends | February 28, 2027 |
| Registry record updated | August 5, 2025 |
| Trial ID | NCT06858553 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Adults (over 18) with a confirmed diagnosis of Crohn's disease who are planning bowel resection for an ileal or colonic stricture and who can undergo MRI with clear MRE images suitable for segmentation are ideal candidates.
✕Probably not a fit
Patients who cannot have MRI, whose MRE scans are poor quality or contain artifacts, who have anastomotic strictures, or whose lesions are complicated by other diseases are unlikely to benefit from this project.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This is a prospective multicenter observational effort enrolling about 234 adults with Crohn's disease who require ileal or colonic resection for strictures. Preoperative MRE scans will be paired with histological analysis of the resected bowel to train and validate a deep learning model that can detect and grade full-thickness intestinal fibrosis. Participating centers will follow a centralized quality assurance plan, including routine monitoring, double verification of data and specimens, and periodic audits. The project requires clear target-segment boundaries on MRE to enable semi-automatic or automatic segmentation for model development.
Why it matters
Potential benefit: If successful, the AI-enhanced MRE tool could allow noninvasive, more accurate detection of full-thickness bowel fibrosis to help guide medical versus surgical treatment decisions.
How similar studies have gone: Prior radiomics and deep learning work on CT and other imaging modalities has shown promising diagnostic capability, but prospective multicenter development of AI models specifically using MRE matched to histology is relatively novel.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. Patients Over 18 years old with a confirmed diagnosis of CD based on the criteria of ECCO guideline. 2. 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. 3. Clear boundaries of the target bowel tract enable accurate semi-automatic or fully automatic intestinal segmentation Exclusion Criteria: 1. Cannot undergo MRI examination 2. Difficult to obtain suitable tissue after surgery 3. MRE imaging is of poor quality or contains artifacts 4. The target bowel is located at the anastomosis (ie, anastomotic stricture) 5. Intestinal lesions concurrent with other diseases
Where it's running
-
The First Affiliated Hospital,Sun Yat-sen University
Guangzhou, Guangdong, China Recruiting -
Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
Hangzhou, Zhejiang, China Recruiting -
Sixth Affiliated Hospital of Sun Yat-sen University
Guangzhou, Guangdong, China Not yet recruiting -
Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University
Nanjing, Jiangsu, China Not yet recruiting -
Ruijin Hospital, Shanghai Jiaotong University School of Medicine
Huangpu, Shanghai Municipality, China Not yet recruiting
Study contacts
- Study coordinator: Minhu Chen, Professor
- Email: chenminhu@mail.sysu.edu.cn
- Phone: +86 13802957089
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.