Postoperative recurrence remains a major challenge in the long-term management of Crohn’s disease, occurring in up to 70% of patients within 1 year following surgical resection. However, advances in artificial intelligence–enabled imaging and multi-omics approaches may support earlier detection, improved risk stratification, and more individualized monitoring strategies for postoperative recurrence.
In a narrative review, investigators examined current and emerging technologies for assessing postoperative recurrence in Crohn’s disease, focusing on advanced endoscopic imaging, intestinal ultrasound, cross-sectional imaging, molecular biomarkers, and artificial intelligence (AI)-driven data integration.
Limitations of Current Monitoring Approaches
Although ileocolonoscopy remains the reference standard for detecting mucosal recurrence, clinical symptoms often don’t correlate with objective inflammation. Many patients remain asymptomatic despite active disease, whereasonly a minority develop clinical relapse during the first year following surgery.
While fecal calprotectin is widely used as a noninvasive biomarker and demonstrates good sensitivity for endoscopic recurrence, variability in optimal cutoff values and timing limits its reliability as a standalone monitoring tool. The investigators noted that composite strategies combining biomarkers with imaging modalities appear more robust compared with single-measure approaches.
Advances in Endoscopic Imaging
High-resolution endoscopic techniques, including virtual electronic chromoendoscopy and probe-based confocal laser endomicroscopy, allow real-time assessment of mucosal and microvascular features that aren’t visible with standard white-light endoscopy. These modalities enable detection of subtle structural and vascular changes that may precede macroscopic recurrence.
In particular, confocal laser endomicroscopy allows in vivo evaluation of epithelial barrier integrity and microerosions at the anastomotic site. Prior studies summarized in the review showed that specific confocal imaging features were associated with a higher likelihood of subsequent endoscopic and clinical recurrence.
Endocytoscopy, an ultra-high magnification technique, has demonstrated correlation with histopathologic findings in inflammatory bowel disease, although its role in postoperative Crohn’s disease recurrence has not yet been formally evaluated.
Role of Intestinal Ultrasound and Cross-Sectional Imaging
Intestinal ultrasound has emerged as a noninvasive method for monitoring postoperative recurrence, providing transmural assessment of bowel wall thickness, vascularity, and extraintestinal features. The investigators highlighted data showing a strong correlation between intestinal ultrasound findings and endoscopic recurrence, especially when combined with fecal calprotectin testing.
Advanced ultrasound techniques such as contrast-enhanced ultrasound and elastography further improve characterization of inflammatory activity and bowel wall stiffness. Contrast-enhanced ultrasound has demonstrated high diagnostic accuracy in detecting early postoperative inflammatory changes, although its use remains largely confined to specialized centers.
Cross-sectional imaging modalities, including computed tomography enterography and magnetic resonance enterography, provide complementary information on transmural inflammation and extraluminal complications. However, standardized scoring systems for postoperative recurrence using these techniques are still under development and require further validation.
Multi-Omics and AI Integration
Beyond imaging, multi-omics approaches encompassing genomics, transcriptomics, proteomics, metabolomics, and microbiome profiling are uncovering biological pathways associated with postoperative recurrence. Identified mechanisms include intestinal barrier dysfunction, fibrotic remodeling, immune activation, and microbiome dysbiosis.
AI offers the potential to integrate clinical, imaging, histologic, and molecular data into predictive models. The investigators summarized emerging machine-learning applications across endoscopy, ultrasound, cross-sectional imaging, histopathology, and omics analyses. While several models have demonstrated promising performance in lesion detection and risk prediction, most remain exploratory and lack external validation.
The investigators emphasized that AI-enabled multimodal integration could standardize assessments, reduce interobserver variability, and support precision-guided postoperative management.
Implications for Clinical Practice
According to the investigators, widespread implementation of these technologies will require prospective validation, methodologic standardization, and integration into clinical workflows. They noted that distinguishing postsurgical changes from early inflammatory recurrence remains a key challenge, particularly at the anastomotic site.
The review authors concluded that combining advanced imaging with AI and multi-omics data may shift postoperative surveillance from reactive detection to proactive, better personalizing monitoring strategies. Such an approach could support earlier intervention, reduce unnecessary procedures, and improve long-term outcomes in patients with Crohn’s disease.
Source: Gut
