A Prospective, Multi-center Study to Characterize Intestinal Fibrosis in Patients With Crohn's Disease Using MR Enterography-Based Artificial Intelligence

What this Trial Is About

Intestinal fibrotic strictures are a serious complication affecting over half of patients with Crohn's disease (CD). Despite new medications, effective treatments are limited. Current methods like endoscopy cannot fully assess bowel wall fibrosis, and standardized imaging assessments are lacking. This study aims to develop and validate a deep learning model using magnetic resonance enterography (MRE) to better characterize intestinal fibrosis in CD patients requiring bowel resection. Participants will be recruited from multiple medical centers and divided into groups for training and validating the AI model. The study involves collecting multi-parametric MRI data and surgical specimens, followed by advanced image processing and model development using deep learning techniques. The model will assess fibrosis severity and assist clinical decisions by integrating imaging and molecular data. During the study, researchers will collect detailed patient information, imaging data, and surgical specimens. They will monitor fibrosis and inflammation scores, MRI measurements, and blood tests before and after surgery. Regular quality assurance and data verification procedures ensure accuracy. The study will follow patients from enrollment through surgery and data analysis to support improved diagnosis and treatment planning in Crohn's disease.

AI-Assisted MRE for Intestinal Fibrosis in Crohn's Disease