Christof, Michael

Inflammatory bowel disease (IBD) is a group of chronic inflammatory conditions of the gastrointestinal tract resulting from an inappropriate immune response to an altered gut microbiome in genetically predisposed individuals. Endoscopy plays a central role in IBD management, aiding in diagnosis, disease staging, monitoring, and therapeutic guidance. Patients with IBD face an increased risk of colorectal neoplasia due to chronic inflammation. Artificial intelligence (AI)-based systems show promise in detecting and classifying dysplasia and neoplasia during endoscopic evaluation. While there have been several studies on the application of AI to detect and diagnose various types of neoplasia in the non-IBD population, the literature in patients with IBD is limited. We aim to summarize the current evidence on the application of AI technologies to detect IBD-associated neoplasia, highlighting potential benefits, limitations, and future directions. A comprehensive literature search was performed using the PubMed database to identify relevant studies from January 2010 to February 2025. Additional references were identified from the relevant articles’ bibliographies. AI-assisted endoscopy, particularly using machine learning and deep learning techniques, has shown promise in improving lesion detection rates and supporting real-time decision-making. Computer-aided detection systems may increase the sensitivity of dysplasia identification, while computer-aided diagnosis tools can aid in lesion characterization. Early studies suggest that AI can reduce interobserver variability, improve targeting of biopsies, and potentially lead to more personalized surveillance strategies. Although clinical data specific to IBD-related neoplasia remain limited compared to sporadic colorectal neoplasia, the integration of AI into endoscopic practice holds significant potential to enhance dysplasia detection and improve patient outcomes. Continued research, validation in IBD-specific cohorts, and integration with clinical workflows are essential to realize the full impact of AI in this setting.

Abstract Background and Aims: Recent systematic reviews and meta-analyses (SRMAs) have shown inconsistent effectiveness of FMT among IBD patients. This study aimed to appraise the evidence for clinically relevant outcomes with FMT in IBD patients using published SRMAs. Methods: We searched major databases from inception through Nov 2023 to identify SRMAs assessing the effectiveness of FMT in IBD patients. Primary outcomes included clinical remission, clinical response, endoscopic remission/response, a composite endpoint, and adverse effects. We included SRMAs investigating FMT's effect in IBD patients using RCTs and observational studies data. Methodological quality and evidence certainty were assessed using AMSTAR 2 and GRADE. Results: Out of 106 citations, 16 SRMAs were included with varying study sizes (2 to 60 primary studies) and participants (112 to 1169 per SRMA). Five SRMAs assessed FMT in IBD, while 11 focused on Ulcerative Colitis (UC). Seven SRMAs included RCTs only, and nine included both RCTs and observational studies. Methodological quality was critically low in 9 SRMAs (56%) and low in 7 studies (44%). FMT showed clinical remission benefit in all 16 SRMAs, with varying certainty: 3 high, 4 moderate, 4 low, and 5 very low. Endoscopic remission/response was reported in 5 meta-analyses on UC, with 1 high, 3 moderate, and 1 very low certainty. Combined clinical remission and endoscopic response were reported in 3 SRMAs on UC, with 1 low and 2 moderate certainty. Adverse events were reported in 6 SRMAs, with 1 high, 3 moderate, 1 low, and 1 very low certainty. Conclusion: Current evidence shows potential benefits of FMT in IBD, particularly UC, supported by significant associations in 16 meta-analyses. However, poor methodological quality and variability in evidence certainty call for high-quality RCTs to strengthen the evidence.