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

Status

Recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07087418

Enrollment

5000

Registered

2025-07-28

Start date

2025-07-01

Completion date

2026-08-01

Last updated

2026-04-13

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

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

Keywords

Radiology, Imaging, Digestive Diseases, Artificial Intelligence

Brief summary

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.

Interventions

DIAGNOSTIC_TESTVirtual endoscopy model-assisted diagnosis

Using the virtual endoscopy model to aid diagnosis

Study design

Observational model

OTHER

Time perspective

OTHER

Eligibility

Sex/Gender

ALL

Healthy volunteers

Yes

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

Design outcomes

Primary

Measure	Time frame	Description
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.

Countries

China

Contacts

CONTACTXuehua Li

lxueh@mail.sysu.edu.cn13580364103

CONTACTYaoqi Ke

keyq3@mail2.sysu.edu.cn18316712708

PRINCIPAL_INVESTIGATORXuehua Li

Sun Yat-sen University First Affiliated Hospital Department of Radiology

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Apr 14, 2026