University of Missouri, Department of Medicine, Division of Gastroenterology and Hepatology, Columbia, MO, United States.
Indiana University School of Medicine, Department of Medicine, Division of Gastroenterology and Hepatology, Indianapolis, IN, United States.
Best Pract Res Clin Gastroenterol. 2021 Jun-Aug;52-53:101736. doi: 10.1016/j.bpg.2021.101736. Epub 2021 Feb 22.
The American Society for Gastrointestinal Endoscopy (ASGE) has proposed the "resect-and-discard" and "diagnose-and-leave" strategies for diminutive colorectal polyps to reduce the costs of unnecessary polyp resection and pathology evaluation. However, the diagnostic thresholds set by these guidelines are not always met in community practice. To overcome this sub-optimal performance, artificial intelligence (AI) has been applied to the field of endoscopy. The incorporation of deep learning algorithms with AI models resulted in highly accurate systems that match the expert endoscopists' optical biopsy and exceed the ASGE recommended thresholds. Recent studies have demonstrated that the integration of AI in clinical practice results in significant improvement in endoscopists' diagnostic accuracy while reducing the time to make a diagnosis. Yet, several points need to be addressed before AI models can be successfully implemented in clinical practice. In this review, we summarize the recent literature on the application of AI for characterization of colorectal polyps, and review the current limitation and future directions for this field.
美国胃肠内镜学会（ASGE）提出了针对微小结直肠息肉的“切除并丢弃”和“诊断并保留”策略，以降低不必要的息肉切除和病理评估的成本。然而，这些指南设定的诊断阈值在社区实践中并不总是能够达到。为了克服这种次优表现，人工智能（AI）已经应用于内镜领域。将深度学习算法与 AI 模型相结合，产生了高度准确的系统，与专家内镜医师的光学活检相匹配，并超过了 ASGE 推荐的阈值。最近的研究表明，将 AI 融入临床实践可显著提高内镜医师的诊断准确性，同时缩短诊断时间。然而，在 AI 模型能够成功应用于临床实践之前，还需要解决几个问题。在这篇综述中，我们总结了最近关于 AI 用于结直肠息肉特征描述的文献，并回顾了该领域目前的局限性和未来的方向。
