ReviewGut2026
Shaping the future of postoperative recurrence in Crohn's disease: personalised approaches with AI-enabled imaging and multi-omics.
Review in Gut, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.