AI-Driven Multimodal Imaging Integration for Diagnosis and Prognostication of Digestive System Diseases

Sun Yat-sen University

Status

Enrolling

Conditions

Radiology

Imaging

AI (Artificial Intelligence)

Digestive Diseases

Treatments

Diagnostic Test: Kaplan-Meier analysis with log-rank testing

Study type

Observational

Funder types

Other

Identifiers

NCT07087418

82270693

Details and patient eligibility

About

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.

Enrollment

5,000 estimated patients

Sex

All

Volunteers

Accepts Healthy Volunteers

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