AI Tool Reads MR Scans to Detect Scarring in Crohn's Disease
Tests an AI tool on MR scans to find bowel scarring in Crohn's disease.
Plain-language title generated from the registry record
Registry title: AI-Assisted MRE for Intestinal Fibrosis in Crohn's Disease
Key facts
- Where
- Guangzhou, Hangzhou, Huangpu, and Nanjing, ChinaSee the 5 sites
Could this be for you?
Answer for yourself. This is not a decision: only the study team can say whether you can take part.
- Are you between 18 and 75 years old?
Source: NCT06858553, who can take part
Where in China
- Sixth Affiliated Hospital of Sun Yat-sen UniversityGuangzhou, ChinaNot yet recruiting
- The First Affiliated Hospital, Sun Yat-sen UniversityGuangzhou, ChinaTaking people
- Sir Run Run Shaw Hospital Zhejiang University School of MedicineHangzhou, ChinaTaking people
- Ruijin Hospital, Shanghai Jiaotong University School of MedicineHuangpu, ChinaNot yet recruiting
- Jinling Hospital, Affiliated Hospital of Medical School, Nanjing UniversityNanjing, ChinaNot yet recruiting
Questions to ask the study team
- How often do I need to come in, and for how long each time?
- Are travel costs paid?
- Can I keep my own doctor?
- What happens if I want to stop?
Trial details
- Who runs it
- Minhu Chen
- Trial code
- NCT06858553
- Record updated
- 5 August 2025
- Original description
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.Read full original descriptionShow less
A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease (CD) Using MR Enterography (MRE)-Based Artificial Intelligence
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.
Inclusion: 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: Difficult to obtain suitable tissue after surgery; Intestinal lesions concurrent with other diseases; Cannot undergo MRI examination; MRE imaging is of poor quality or contains artifacts; The target bowel is located at the anastomosis (ie, anastomotic stricture).