Testoni Sabrina Gloria Giulia, Albertini Petroni Guglielmo, Annunziata Maria Laura, Dell'Anna Giuseppe, Puricelli Michele, Delogu Claudia, Annese Vito
Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, Vita-Salute San Raffaele University, San Donato Milanese, 20097 Milan, Italy.
Unit of Gastroenterology and Digestive Endoscopy, Scientific Institute for Research, Hospitalization and Healthcare Policlinico San Donato, San Donato Milanese, 20097 Milan, Italy.
Diagnostics (Basel). 2025 Apr 1;15(7):905. doi: 10.3390/diagnostics15070905.
Inflammatory bowel diseases (IBDs), comprising Crohn's disease (CD) and ulcerative colitis (UC), are chronic immune-mediated inflammatory diseases of the gastrointestinal (GI) tract with still-elusive etiopathogeneses and an increasing prevalence worldwide. Despite the growing availability of more advanced therapies in the last two decades, there are still a number of unmet needs. For example, the achievement of mucosal healing has been widely demonstrated as a prognostic marker for better outcomes and a reduced risk of dysplasia and cancer; however, the accuracy of endoscopy is crucial for both this aim and the precise and reproducible evaluation of endoscopic activity and the detection of dysplasia. Artificial intelligence (AI) has drastically altered the field of GI studies and is being extensively applied to medical imaging. The utilization of deep learning and pattern recognition can help the operator optimize image classification and lesion segmentation, detect early mucosal abnormalities, and eventually reveal and uncover novel biomarkers with biologic and prognostic value. The role of AI in endoscopy-and potentially also in histology and imaging in the context of IBD-is still at its initial stages but shows promising characteristics that could lead to a better understanding of the complexity and heterogeneity of IBDs, with potential improvements in patient care and outcomes. The initial experience with AI in IBDs has shown its potential value in the differentiation of UC and CD when there is no ileal involvement, reducing the significant amount of time it takes to review videos of capsule endoscopy and improving the inter- and intra-observer variability in endoscopy reports and scoring. In addition, these initial experiences revealed the ability to predict the histologic score index and the presence of dysplasia. Thus, the purpose of this review was to summarize recent advances regarding the application of AI in IBD endoscopy as there is, indeed, increasing evidence suggesting that the integration of AI-based clinical tools will play a crucial role in paving the road to precision medicine in IBDs.
炎症性肠病（IBD）包括克罗恩病（CD）和溃疡性结肠炎（UC），是胃肠道的慢性免疫介导炎症性疾病，其病因仍不明确，且在全球范围内患病率呈上升趋势。尽管在过去二十年中越来越多先进疗法可供使用，但仍存在一些未满足的需求。例如，黏膜愈合已被广泛证明是预后较好以及发育异常和癌症风险降低的预后标志物；然而，内镜检查的准确性对于实现这一目标以及精确且可重复地评估内镜活动和检测发育异常至关重要。人工智能（AI）极大地改变了胃肠病学研究领域，并被广泛应用于医学成像。深度学习和模式识别的应用可以帮助操作人员优化图像分类和病变分割，检测早期黏膜异常，并最终发现具有生物学和预后价值的新型生物标志物。AI在IBD内镜检查中的作用——可能在组织学和成像方面也是如此——仍处于初始阶段，但显示出有前景的特性，这可能有助于更好地理解IBD的复杂性和异质性，并有可能改善患者护理和治疗结果。IBD中AI的初步经验表明，在无回肠受累时，AI在区分UC和CD方面具有潜在价值，减少了查看胶囊内镜视频所需的大量时间，并提高了内镜检查报告和评分中观察者间和观察者内的一致性。此外，这些初步经验还揭示了预测组织学评分指数和发育异常存在的能力。因此，本综述的目的是总结AI在IBD内镜检查中应用的最新进展，因为确实有越来越多的证据表明，基于AI的临床工具的整合将在IBD精准医学之路上发挥关键作用。
