Skip to main content
OpenTrials
Recruiting

NCT Number: NCT07087418

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

The goal of this observational, retrospective and prospective study is 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. To this end, the investigators retrospectively enrolled imaging, endoscopic, and clinical data from 21 centers across China to construct and iteratively optimize the AI model. The model's performance will be prospectively validated in two centers, and its accuracy in lesion localization will be verified through real-world deployment in endoscopy suites.

Recruiting

Interested in participating?

Request Info

Key information

Conditions

Digestive Diseases AI (Artificial Intelligence) Digestive System Diseases Imaging Radiology

Sex eligibility

All sexes

Study type

Observational

Primary location

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Virtual endoscopy model-assisted diagnosis

Diagnostic Test

Using the virtual endoscopy model to aid diagnosis

Primary outcomes

  1. The area under the ROC curve (AUC) to assess the performance of diagnostic model.

    Time frame: 6 months

    After baseline MR or CT scanning, patients were followed up.

Study contacts

Contact information is provided by the study sponsor or research team.

Xuehua Li

CONTACT

[email protected]

13580364103

Yaoqi Ke

CONTACT

[email protected]

18316712708

Sponsors and collaborators

Lead sponsor

First Affiliated Hospital, Sun Yat-Sen University

Other

Registry information

Important dates

Study start
2025
Primary completion
2026
Study completion
2026
First posted
Jul 28, 2025
Registry last updated
Apr 13, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.