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.
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.
・Suboptimal imaging quality (e.g., low-dose artifacts, metal artifacts)
Inadequate bowel preparation for endoscopy
Incomplete examinations due to poor tolerance
This retrospective and prospective study included patients with digestive diseases. For the retrospective component, the investigators enrolled patients from 21 hospitals across China, including the First Affiliated Hospital of Sun Yat-sen University. For the prospective component, the investigators enrolled patients from the First Affiliated Hospital of Sun Yat-sen University and the Third Affiliated Hospital of Guangzhou Medical University.
Other
Other
Virtual endoscopy model-assisted diagnosis
Diagnostic testUsing the virtual endoscopy model to aid diagnosis
The area under the ROC curve (AUC) to assess the performance of diagnostic model.
After baseline MR or CT scanning, patients were followed up.
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