Copyright ©The Author(s) 2022. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastrointest Endosc. Jun 28, 2022; 3(3): 31-43
Published online Jun 28, 2022. doi: 10.37126/aige.v3.i3.31
Artificial intelligence and machine learning in colorectal cancer
Muhammad Awidi, Arindam Bagga
Muhammad Awidi, Internal Medicine, Beth Israel Lahey Health, Burlington, MA 01805, United States
Arindam Bagga, Internal Medicine, Tufts Medical Center, Boston, MA 02111, United States
Author contributions: Awidi M and Bagga A contributed equally to the work; All authors have read and approve the final manuscript.
Conflict-of-interest statement: There is no conflict of interest associated with any of the authors who contributed their efforts to this manuscript.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Muhammad Awidi, MD, Academic Fellow, Internal Medicine, Beth Israel Lahey Health, 41 Mall Road, Burlington, MA 01805, United States.
Received: January 17, 2022
Peer-review started: January 17, 2022
First decision: March 8, 2022
Revised: March 24, 2022
Accepted: June 20, 2022
Article in press: June 20, 2022
Published online: June 28, 2022

Colorectal cancer (CRC) is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US. Artificial intelligence (AI) with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types. This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them. Furthermore, we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions. Considering the evolving transition to more personalized and tailored treatment strategies for CRC, the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes. We also discuss the constraints and limitations of the use of AI systems. While the medical profession remains enthusiastic about the future of AI and machine learning, large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used.

Keywords: Artificial intelligence, Machine learning, Colonic polyps, Colorectal neoplasms, Computer-aided diagnosis, Precision oncology

Core Tip: Artificial intelligence (AI) and its potential in diagnosing colorectal cancer have been the subject of various reviews in the literature. However, this review reports the most recent discoveries and studies on artificial and machine learning in colorectal cancer screening, diagnosis, and treatment, as well as the future roles that AI applications may play in assisting in the treatment of colorectal cancer. Furthermore, this review talks about prospects and constraints for the use of AI systems, as well as the need for large-scale randomized clinical trials to examine AI algorithms before they can be implemented.