AI-Driven Multimodal Imaging Integration for Diagnosis and Prognostication of Digestive System Diseases
First Affiliated Hospital, Sun Yat-Sen University
5,000 participants
Jul 1, 2025
OBSERVATIONAL
Conditions
Summary
The goal of this observational study is to to develop a noninvasive disease assessment system by leveraging artificial intelligence (AI) to comprehensively analyze multi-modal imaging features, including magnetic resonance enterography (MRE) and computed tomography enterography (CTE), for the diagnosis and prognostication of digestive diseases. Participants will be randomly assigned to either conventional endoscopy or virtual endoscopy groups. The predictive performance of both groups for prognostic indicators, such as clinical remission rate and recurrence risk, will be compared during follow-up to verify the non-inferiority of the virtual endoscopy group.
Eligibility
Inclusion Criteria5
- Patients with multimodal-confirmed diagnoses (clinical, imaging, endoscopic, and pathological) of:
- Inflammatory bowel disease (IBD; Crohn's disease or ulcerative colitis)
- Intestinal tuberculosis
- Behçet's disease
- Availability of ≥1 technically adequate CT or MR scan with high-quality colonoscopy performed within ±1 month of imaging.
Exclusion Criteria3
- ・Suboptimal imaging quality (e.g., low-dose artifacts, metal artifacts)
- Inadequate bowel preparation for endoscopy
- Incomplete examinations due to poor tolerance
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Interventions
The non-inferiority of the virtual endoscopy group will be verified by comparing the predictive performance for prognostic indicators (e.g., clinical remission rate, recurrence risk) between the two groups during follow-up.
Locations(1)
View Full Details on ClinicalTrials.gov
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NCT07087418