Published online Oct 21, 2020. doi: 10.3748/wjg.v26.i39.5911
Peer-review started: July 27, 2020
First decision: August 8, 2020
Revised: August 18, 2020
Accepted: September 23, 2020
Article in press: September 23, 2020
Published online: October 21, 2020
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.
Core Tip: Artificial intelligence is an emerging technology which application is rapidly increasingin numerous medical fields. The several applications of artificial intelligence in gastroenterology are showing promising results, especially in the setting of gastrointestinal oncology. Among these, the techniques able to increase the Adenoma detection rate will play a key role in reducing the colorectal cancer incidence and its related mortality caused by undetected or missclassified interval cancers. Furthermore, a significant reduction in costs is also expected. In addition the assistance of the Computer-aided detection and diagnosis systems will lead to a reduction of the examination time and therefore an optimization of the endoscopic schedule.