PDF(Pubmed)
Abstract：
Recent medical literature shows that the application of artificial intelligence (AI) models in gastrointestinal pathology is an exponentially growing field, with promising models that show very high performances. Regarding inflammatory bowel disease (IBD), recent reviews demonstrate promising diagnostic and prognostic AI models. However, studies are generally at high risk of bias (especially in AI models that are image-based). The creation of specific AI models that improve diagnostic performance and allow the establishment of a general prognostic forecast in IBD is of great interest, as it may allow the stratification of patients into subgroups and, in turn, allow the creation of different diagnostic and therapeutic protocols for these patients. Regarding surgical models, predictive models of postoperative complications have shown great potential in large-scale studies. In this work, the authors present the development of a predictive algorithm for early post-surgical complications in Crohn\'s disease based on a Random Forest model with exceptional predictive ability for complications within the cohort. The present work, based on logical and reasoned, clinical, and applicable aspects, lays a solid foundation for future prospective work to further develop post-surgical prognostic tools for IBD. The next step is to develop in a prospective and multicenter way, a collaborative path to optimize this line of research and make it applicable to our patients.
摘要：
最近的医学文献表明，人工智能（AI）模型在胃肠道病理学中的应用是一个指数增长的领域，有前途的模型，表现出非常高的性能。关于炎症性肠病（IBD），最近的评论证明了有希望的诊断和预后AI模型。然而,研究通常存在较高的偏差风险(特别是在基于图像的人工智能模型中)。创建特定的AI模型以提高诊断性能并允许在IBD中建立一般的预后预测非常感兴趣，因为它可以将患者分为亚组，反过来,允许为这些患者创建不同的诊断和治疗方案。关于手术模型，术后并发症预测模型在大规模研究中显示出巨大潜力.在这项工作中,作者介绍了基于随机森林模型的克罗恩病术后早期并发症预测算法的开发,该模型对队列中的并发症具有出色的预测能力.目前的工作,基于逻辑和推理，临床,和适用方面，为今后进一步开发IBD术后预后工具的前瞻性工作奠定了坚实的基础。下一步是以前瞻性和多中心的方式发展，这是一条优化这条研究路线并使其适用于我们的患者的协作路径。
