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Artificial Intelligence for Colorectal Polyp Detection and Characterization

  • Endoscopy (P Siersema, Section Editor)
  • Published:
Current Treatment Options in Gastroenterology Aims and scope Submit manuscript

Abstract

Purpose of review

To elucidate the advantages and limitations of existing artificial intelligence technologies for colonoscopy by evaluating the currently available eight prospective studies (two for automated polyp detection and six for automated polyp characterization).

Recent findings

AI is expected to mitigate the inherent risk of human error causing a polyp to be missed or mischaracterized by assisting polyp detection and characterization (i.e., pathological prediction). Some of the prospective studies clearly demonstrate the potential for AI to improve adenoma detection rates, which is considered one of the most important quality indicators for colonoscopies, or achieve a > 90% negative predictive value in differentiating diminutive (≤ 5 mm) rectosigmoid adenomas which is considered as a threshold required for optical diagnosis. However, it is also important to consider the negative impacts of AI, such as the deskilling effect on healthcare providers, which has yet to be sufficiently addressed.

Summary

We believe that AI can become standard practice in colonoscopy procedures within several years, given its rapid spread and its expected low implementation cost. However, considering the limited evidence supporting the use of AI for colonoscopy, additional studies should be done to explore the long-term efficacy and safety of AI in colonoscopy and implement robust endpoints such as colorectal cancer incidence and mortality.

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Acknowledgments

We thank Stephanie Knowlton, PhD, from Edanz Group (https://en-author-services.edanzgroup.com/) for editing a draft of this manuscript.

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Authors and Affiliations

  1. Digestive Disease Center, Showa University Northern Yokohama Hospital, 35-1 Chigasaki-tyuo, Tsuzuki-ku, Yokohama, 224-8503, Japan

    Yuichi Mori MD, PhD, Shin-ei Kudo MD, PhD, Masashi Misawa MD, PhD, Kenichi Takeda MD, PhD & Toyoki Kudo MD, PhD

  2. Graduate School of Informatics, Nagoya University, Nagoya, Japan

    Hayato Itoh PhD, Masahiro Oda PhD & Kensaku Mori PhD

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  1. Yuichi Mori MD, PhD
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  2. Shin-ei Kudo MD, PhD
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Correspondence to Yuichi Mori MD, PhD.

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Conflict of Interest

YM, SK, and MM have received speaking honoraria from Olympus Corp. KM received research funding from Cybernet Corp. None of the other authors has conflicts of interest relating to the present study/paper.

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Mori, Y., Kudo, Se., Misawa, M. et al. Artificial Intelligence for Colorectal Polyp Detection and Characterization. Curr Treat Options Gastro 18, 200–211 (2020). https://doi.org/10.1007/s11938-020-00287-x

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