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

Status: Recruiting
Location: See location...
Intervention Type: Diagnostic test
Study Type: Observational
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
Participation Requirements
Sex: All
Healthy Volunteers: t
View:

• 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.

Locations
Other Locations
China
XploreMET v3.0 system
RECRUITING
Shanghai
Contact Information
Primary
Xuehua Li
lxueh@mail.sysu.edu.cn
13580364103
Backup
Yaoqi Ke
keyq3@mail2.sysu.edu.cn
18316712708
Time Frame
Start Date: 2025-07-01
Estimated Completion Date: 2026-08-01
Participants
Target number of participants: 5000
Treatments
Conventional endoscopy group
Prognostic indicators (e.g., clinical remission rate, recurrence risk) were predicted using conventional endoscopy at baseline.
Virtual endoscopy group
Prognostic indicators were predicted using virtual endoscopy at baseline.
Related Therapeutic Areas
Sponsors
Leads: First Affiliated Hospital, Sun Yat-Sen University

This content was sourced from clinicaltrials.gov