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CC BY 4.0 · Journal of Digestive Endoscopy 2023; 14(04): 239-242
DOI: 10.1055/s-0043-1778059
Technical note

Artificial Intelligence in Colonoscopic Polyp Detection and Characterization: Merging Computer Technology and Endoscopic Skill for Better Patient Care

Uday C. Ghoshal
1   Department of Gastroenterology, Institute of Gastrosciences & Liver Transplantation, Apollo Multispeciality Hospitals, Kolkata, West Bengal, India
,
Saikat Chakrabarti
2   CSIR-Indian Institute of Chemical Biology, Salt Lake, Kolkata, West Bengal, India
,
Mahesh K. Goenka
1   Department of Gastroenterology, Institute of Gastrosciences & Liver Transplantation, Apollo Multispeciality Hospitals, Kolkata, West Bengal, India
› Author Affiliations Funding None.
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Abstract

Artificial intelligence (AI) is a computer technology for mathematical modeling that uses nonlinear statistical analysis. While multilayer perceptron network is used for prediction of clinical outcome, convolutional neural network is used for detection of lesion in an image and its classification. In this issue of the journal, an article reviewed the impact of AI in colorectal polyp detection and characterization. This is an upcoming area of gastroenterology, which has already reached the doorstep of practicing clinicians and in the near future, it may bring a paradigm shift in clinical practice. It is expected that this thought-provoking review will stimulate endoscopists to take up research in this important field of application of an AI-based computer technology for endoscopic detection of gastrointestinal lesions.

Keywords

Deep learning - Artificial neural network - Convolutional neural network - Nonlinear modeling - Colon cancer

Authors' Contributions

U.C.G.: Literature review and review of the paper in relation to which this editorial is written, and writing the first draft of the paper. S.C.: Critical input while writing the first draft and subsequent editing of the paper. M.K.G.: Critical input and editing of the manuscript.




Publication History

Article published online:
28 December 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

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