Overview
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 Criteria:
- 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 Criteria:
- ・Suboptimal imaging quality (e.g., low-dose artifacts, metal artifacts)
- Inadequate bowel preparation for endoscopy
- Incomplete examinations due to poor tolerance