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Review
. 2021 Oct 14;27(38):6399-6414.
doi: 10.3748/wjg.v27.i38.6399.

Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Michelle Viscaino  1 , Javier Torres Bustos  1 , Pablo Muñoz  2 , Cecilia Auat Cheein  3 , Fernando Auat Cheein  4
Affiliations
Review

Artificial intelligence for the early detection of colorectal cancer: A comprehensive review of its advantages and misconceptions

Michelle Viscaino et al. World J Gastroenterol. .

Abstract

Colorectal cancer (CRC) was the second-ranked worldwide type of cancer during 2020 due to the crude mortality rate of 12.0 per 100000 inhabitants. It can be prevented if glandular tissue (adenomatous polyps) is detected early. Colonoscopy has been strongly recommended as a screening test for both early cancer and adenomatous polyps. However, it has some limitations that include the high polyp miss rate for smaller (< 10 mm) or flat polyps, which are easily missed during visual inspection. Due to the rapid advancement of technology, artificial intelligence (AI) has been a thriving area in different fields, including medicine. Particularly, in gastroenterology AI software has been included in computer-aided systems for diagnosis and to improve the assertiveness of automatic polyp detection and its classification as a preventive method for CRC. This article provides an overview of recent research focusing on AI tools and their applications in the early detection of CRC and adenomatous polyps, as well as an insightful analysis of the main advantages and misconceptions in the field.

Keywords: Artificial intelligence; Colorectal cancer; Colorectal polyps; Deep learning; Machine learning; Medical images.

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Conflict of interest statement

Conflict-of-interest statement: The authors deny any conflict of interest.

Figures

Figure 1
Figure 1
Artificial intelligence is a set of fields that are combining to improve tasks that involve human cognitive functions such as learning, reasoning and self-correction.
Figure 2
Figure 2
Comparison of traditional and AI-based computer-aided systems on colonoscopy examination.
See this image and copyright information in PMC

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