Clinical Research Directory

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

Sponsor: First Affiliated Hospital, Sun Yat-Sen University

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, we 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. 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.

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