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

Recruiting

• Conditions: Digestive Diseases; Radiology; AI (Artificial Intelligence); Imaging

• Registration Number: NCT07087418
• Lead Sponsor: First Affiliated Hospital, Sun Yat-Sen University

Brief 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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2025-07
• Study Start: 2025-07-01
• Study First Submitted: 2025-07-20
• Study First Submitted QC: 2025-07-20
• Last Update Submitted: 2025-07-20
• Study First Posted: 2025-07-28
• Last Update Posted: 2025-07-28
• Primary Completion: 2026-08-01
• Study Completion: 2026-08-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 5000

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
The area under the ROC curve (AUC) to assess the performance of diagnostic model.	6 months	After baseline MR or CT scanning, patients were followed up.

Trial Locations

Locations (1)

XploreMET v3.0 system; 🇨🇳 Shanghai, China