Journal of the Anus, Rectum and Colon
Online ISSN : 2432-3853
ISSN-L : 2432-3853
Review Article
How Far Will Clinical Application of AI Applications Advance for Colorectal Cancer Diagnosis?
Yuichi MoriShin-ei KudoMasashi MisawaKenichi TakedaToyoki KudoHayato ItohMasahiro OdaKensaku Mori
Author information
JOURNALS OPEN ACCESS

2020 Volume 4 Issue 2 Pages 47-50

Details
Abstract

Integrating artificial intelligence (AI) applications into colonoscopy practice is being accelerated as deep learning technologies emerge. In this field, most of the preceding research has focused on polyp detection and characterization, which can mitigate inherent human errors accompanying colonoscopy procedures. On the other hand, more challenging research areas are currently capturing attention: the automated prediction of invasive cancers. Colorectal cancers (CRCs) harbor potential lymph node metastasis when they invade deeply into submucosal layers, which should be resected surgically rather than endoscopically. However, pretreatment discrimination of deeply invasive submucosal CRCs is considered difficult, according to previous prospective studies (e.g., <70% sensitivity), leading to an increased number of unnecessary surgeries for large adenomas or slightly invasive submucosal CRCs. AI is now expected to overcome this challenging hurdle because it is considered to provide better performance in predicting invasive cancer than non-expert endoscopists. In this review, we introduce five relevant publications in this area. Unfortunately, progress in this research area is in a very preliminary phase, compared to that of automated polyp detection and characterization, because of the lack of number of invasive CRCs used for machine learning. However, this issue will be overcome with more target images and cases. The research field of AI for invasive CRCs is just starting but could be a game changer of patient care in the near future, given rapidly growing technologies, and research will gradually increase.

Information related to the author
© 2020 The Japan Society of Coloproctology

JARC is an Open Access journal distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. Anyone may download, reuse, copy, reprint, or distribute articles published in the Journal for non-profit purposes if they cite the original authors and source properly. If anyone remixes, transforms, or builds upon the material, you may not distribute the modified material.
https://creativecommons.org/licenses/by-nc-nd/4.0/
Next article
feedback
Top