Next-Generation Endoscopy in Inflammatory Bowel Disease

Endoscopic healing is recognized as a primary treatment goal in Inflammatory Bowel Disease (IBD). However, endoscopic remission may not reflect histological remission, which is crucial to achieving favorable long-term outcomes. The development of new advanced techniques has revolutionized the field of IBD assessment and management. These tools can accurately assess vascular and mucosal features, drawing endoscopy closer to histology. Moreover, they can enhance the detection and characterization of IBD-related dysplasia. Given the persistent challenge of interobserver variability, a more standardized approach to endoscopy is warranted, and the integration of artificial intelligence (AI) holds promise for addressing this limitation. Additionally, although molecular endoscopy is still in its infancy, it is a promising tool to forecast response to therapy. This review provides an overview of advanced endoscopic techniques, including dye-based and dye-less chromoendoscopy, and in vivo histological examinations with probe-based confocal laser endomicroscopy and endocytoscopy. The remarkable contribution of these tools to IBD management, especially when integrated with AI, is discussed. Specific attention is given to their role in improving disease assessment, detection, and characterization of IBD-associated lesions, and predicting disease-related outcomes.

Automated summary

Endoscopic healing is an important treatment goal in Inflammatory Bowel Disease (IBD), but it may not reflect histological remission. New advanced techniques in IBD assessment and management have improved the accuracy of endoscopy in assessing vascular and mucosal features and detecting IBD-related dysplasia. The integration of artificial intelligence (AI) shows promise in addressing the challenge of interobserver variability. Molecular endoscopy is still in its early stages but offers potential for predicting response to therapy. This review discusses the contribution of advanced endoscopic techniques and their integration with AI in improving disease assessment, lesion detection, and outcome prediction in IBD management.