AI-Enabled Imaging for Predicting Postoperative Recurrence in Crohn’s Disease: Gut, March 2026

Postoperative recurrence (POR) remains a major challenge in Crohn’s disease (CD), occurring in up to 70% of patients within the first year after intestinal resection. Current surveillance strategies—primarily ileocolonoscopy and fecal calprotectin—have limited accuracy and lack standardized monitoring algorithms.

Emerging technologies are reshaping the approach to POR detection. Advanced endoscopic imaging techniques, including confocal laser endomicroscopy and endocytoscopy, allow real-time microscopic evaluation of the anastomosis and early mucosal changes. Intestinal ultrasound and cross-sectional imaging provide non-invasive transmural assessment, improving monitoring beyond mucosal disease.

Simultaneously, multi-omics approaches (genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling) are identifying biological pathways associated with recurrence risk. Integration of these datasets using artificial intelligence (AI) may enable predictive multimodal models that combine clinical, imaging, and molecular data.

Such AI-driven strategies could allow personalized risk stratification, earlier detection of recurrence, and tailored postoperative therapy, moving Crohn’s disease management toward precision medicine. However, prospective validation and clinical workflow integration are still required.