Biophysics inspired artificial intelligence for colorectal cancer characterization

Abstract

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.