Shaping the future of postoperative recurrence in Crohn's disease: personalised approaches with AI-enabled imaging and multi-omics.
Authors
Affiliations (7)
Affiliations (7)
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland [email protected].
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, Cork, Ireland.
- Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy.
- Department of Internal Medicine and Medical Therapeutics, University of Pavia, Pavia, Italy.
- Instituto de Investigaciòn e Innovaciòn en Bioingenierìa, Universitat Politecnica de Valencia, Valencia, Spain.
- School of Engineering Computer Science and Informatics, London South Bank University, London, UK.
- Gastroenterology Unit, Department of Public Health, University of Naples Federico II, Naples, Italy.
Abstract
Postoperative recurrence (POR) is a major challenge in the long-term management of Crohn's disease (CD), affecting up to 70% of patients within the first year after surgical resection. The multifactorial pathogenesis of POR complicates prevention, while evolving surgical techniques and different anastomotic configurations further hinder accurate prediction and monitoring.Current surveillance strategies, including standard ileocolonoscopy and faecal calprotectin, remain limited by suboptimal accuracy, the absence of validated scoring systems and the lack of standardised monitoring intervals. Recent advances in high-resolution endoscopic imaging, such as confocal laser endomicroscopy and endocytoscopy, enable real-time, in vivo microstructural assessment of the anastomosis, offering opportunities for earlier and more precise detection of recurrence. In parallel, developments in intestinal ultrasound and cross-sectional imaging are reshaping non-invasive monitoring by providing transmural evaluation. Beyond imaging, multiomics approaches, spanning genomics, transcriptomics, proteomics, metabolomics and metagenomics, are uncovering novel biological pathways linked to POR, providing new mechanistic insights.Artificial intelligence (AI) has the potential to integrate clinical, endoscopic, imaging and omics data into predictive multimodal models for POR, supporting individualised risk stratification, early detection and personalised treatment strategies. While promising, these innovations require prospective validation, methodological standardisation and integration into clinical workflows before translation into routine practice.This review summarises the current understanding of POR, highlights emerging diagnostic and monitoring technologies and explores how AI-enabled endoscopy and multi-omics approaches may transform future management, paving the way towards precision medicine for POR in CD.