Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Artif Intell Gastroenterol. Jun 28, 2021; 2(3): 77-84
Published online Jun 28, 2021. doi: 10.35712/aig.v2.i3.77
Biophysics inspired artificial intelligence for colorectal cancer characterization
Niall P Hardy, Jeffrey Dalli, Pól Mac Aonghusa, Peter M Neary, Ronan A Cahill
Niall P Hardy, Jeffrey Dalli, Ronan A Cahill, UCD Centre for Precision Surgery, Dublin 7 D07 Y9AW, Ireland
Pól Mac Aonghusa, IBM Research, IBM Research Ireland, Dublin 15 D15 HN66, Ireland
Peter M Neary, Department of Surgery, University Hospital Waterford, University College Cork, Waterford X91 ER8E, Ireland
Ronan A Cahill, Department of Surgery, Mater Misericordiae University Hospital (MMUH), Dublin 7, Ireland
Author contributions: Hardy NP, Dalli J, Mac Aonghusa P, Neary PM and Cahill RA were all involved in the research, planning and construction of this piece.
Conflict-of-interest statement: Cahill RA receives speakers fees from Stryker Corp, Johnson and Johnson/Ethicon and Olympus, consultancy fees from Touch Surgery and DistalMotion and research funding from Intuitive Surgery and holds research funding from the Irish Government in collaboration with IBM Research in Ireland and Deciphex and from EU Horizon 2020 with Palliare. Hardy NP and Dalli J are employed as researchers in this collaboration.
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: Ronan A Cahill, FRCS, MBChB, MD, Professor, Department of Surgery, Mater Misericordiae University Hospital (MMUH), 47 Eccles Street, Dublin 7, Ireland.
Received: January 28, 2021
Peer-review started: January 29, 2021
First decision: May 2, 2021
Revised: May 21, 2021
Accepted: June 18, 2021
Article in press: June 18, 2021
Published online: June 28, 2021

Over the last ten years artificial intelligence (AI) methods have begun to pervade even the most common everyday tasks such as email filtering and mobile banking. While the necessary quality and safety standards may have understandably slowed the introduction of AI to healthcare when compared with other industries, we are now beginning to see AI methods becoming more available to the clinician in select settings. In this paper we discuss current AI methods as they pertain to gastrointestinal procedures including both gastroenterology and gastrointestinal surgery. The current state of the art for polyp detection in gastroenterology is explored with a particular focus on deep leaning, its strengths, as well as some of the factors that may limit its application to the field of surgery. The use of biophysics (utilizing physics to study and explain biological phenomena) in combination with more traditional machine learning is also discussed and proposed as an alternative approach that may solve some of the challenges associated with deep learning. Past and present uses of biophysics inspired AI methods, such as the use of fluorescence guided surgery to aid in the characterization of colorectal lesions, are used to illustrate the role biophysics-inspired AI can play in the exciting future of the gastrointestinal proceduralist.

Keywords: Gastroenterology, Artificial intelligence, Gastrointestinal surgery, Deep learning, Biophysics, Machine learning

Core Tip: In this piece we provide an overview of current state of the art in gastroenterology and gastrointestinal surgery. We discuss current deep learning artificial intelligence methods for colorectal lesion detection and characterization as well as exploring biophysics inspired artificial intelligence methods and the potential role they can play in the future of gastroenterological practice.