Sinagra Emanuele, Badalamenti Matteo, Maida Marcello, Spadaccini Marco, Maselli Roberta, Rossi Francesca, Conoscenti Giuseppe, Raimondo Dario, Pallio Socrate, Repici Alessandro, Anderloni Andrea
Gastroenterology and Endoscopy Unit, Fondazione Istituto San Raffaele Giglio, Cefalù 90015, Italy.
Digestive Endoscopy Unit, Division of Gastroenterology, Humanitas Clinical and Research Center IRCCS, Rozzano 20089, Italy.
World J Gastroenterol. 2020 Oct 21;26(39):5911-5918. doi: 10.3748/wjg.v26.i39.5911.
Colonoscopy remains the standard strategy for screening for colorectal cancer around the world due to its efficacy in both detecting adenomatous or pre-cancerous lesions and the capacity to remove them intra-procedurally. Computer-aided detection and diagnosis (CAD), thanks to the brand new developed innovations of artificial intelligence, and especially deep-learning techniques, leads to a promising solution to human biases in performance by guarantying decision support during colonoscopy. The application of CAD on real-time colonoscopy helps increasing the adenoma detection rate, and therefore contributes to reduce the incidence of interval cancers improving the effectiveness of colonoscopy screening on critical outcome such as colorectal cancer related mortality. Furthermore, a significant reduction in costs is also expected. In addition, the assistance of the machine will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule. The aim of this opinion review is to analyze the clinical applications of CAD and artificial intelligence in colonoscopy, as it is reported in literature, addressing evidence, limitations, and future prospects.
由于结肠镜检查在检测腺瘤或癌前病变以及在检查过程中切除这些病变方面的有效性，它仍然是全球范围内结直肠癌筛查的标准策略。得益于人工智能，特别是深度学习技术的全新创新，计算机辅助检测与诊断（CAD）通过在结肠镜检查期间提供决策支持，为解决人类操作中的偏差带来了一个有前景的解决方案。CAD在实时结肠镜检查中的应用有助于提高腺瘤检测率，从而有助于降低间期癌的发生率，提高结肠镜检查筛查在诸如结直肠癌相关死亡率等关键结果方面的有效性。此外，预计成本也会显著降低。此外，机器辅助将减少检查时间，从而优化内镜检查安排。这篇观点综述的目的是分析CAD和人工智能在结肠镜检查中的临床应用，正如文献中所报道的那样，探讨证据、局限性和未来前景。
