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

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.

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