Artificial Intelligence-Aided Endoscopy and Colorectal Cancer Screening

Abstract

Colorectal cancer (CRC) is the third most common cancer worldwide, with the highest incidence reported in high-income countries. However, because of the slow progression of neoplastic precursors, along with the opportunity for their endoscopic detection and resection, a well-designed endoscopic screening program is expected to strongly decrease colorectal cancer incidence and mortality. In this regard, quality of colonoscopy has been clearly related with the risk of post-colonoscopy colorectal cancer. Recently, the development of artificial intelligence (AI) applications in the medical field has been growing in interest. Through machine learning processes, and, more recently, deep learning, if a very high numbers of learning samples are available, AI systems may automatically extract specific features from endoscopic images/videos without human intervention, helping the endoscopists in different aspects of their daily practice. The aim of this review is to summarize the current knowledge on AI-aided endoscopy, and to outline its potential role in colorectal cancer prevention.

Keywords: cancer; colonoscopy; innovation; screening; technology.

Figure 1

Neural networks and subsets of artificial intelligence.

Figure 2

Application of a Computer-Aided Detection (CADe) system (GI-Genius, Medtronic Corp, Minneapolis, MN, USA).

Figure 3

Application of a Computer-Aided Characterization (CADx) system (CAD-EYE, Fujifilm, Japan).

Figure 4

Example of false positive activation of two different Computer-Aided Detection systems due to colonic fold and stool residue.