Artificial Intelligence in Gastrointestinal Endoscopy

Alexander P Abadir, Mohammed Fahad Ali, William Karnes, Jason B Samarasena, Alexander P Abadir, Mohammed Fahad Ali, William Karnes, Jason B Samarasena

Abstract

Artificial intelligence (AI) is rapidly integrating into modern technology and clinical practice. Although in its nascency, AI has become a hot topic of investigation for applications in clinical practice. Multiple fields of medicine have embraced the possibility of a future with AI assisting in diagnosis and pathology applications. In the field of gastroenterology, AI has been studied as a tool to assist in risk stratification, diagnosis, and pathologic identification. Specifically, AI has become of great interest in endoscopy as a technology with substantial potential to revolutionize the practice of a modern gastroenterologist. From cancer screening to automated report generation, AI has touched upon all aspects of modern endoscopy. Here, we review landmark AI developments in endoscopy. Starting with broad definitions to develop understanding, we will summarize the current state of AI research and its potential applications. With innovation developing rapidly, this article touches upon the remarkable advances in AI-assisted endoscopy since its initial evaluation at the turn of the millennium, and the potential impact these AI models may have on the modern clinical practice. As with any discussion of new technology, its limitations must also be understood to apply clinical AI tools successfully.

Keywords: Artificial intelligence; Colonoscopy; Computer assisted diagnosis; Early detection of cancer; Endoscopy.

Conflict of interest statement

Conflicts of Interest: The authors have no financial conflicts of interest.

Figures

Fig. 1.
Fig. 1.
Convolutional neural network (CNN) identification of a polyp. The CNN identifies the edges of a polyp via color and contour patterns. It further expands its recognition, going through multiple pathways to expand the identification area. The CNN identifies a pattern that it suspects as a polyp, tracing the borders of the lesion. Finally, the CNN reports the lesion to the endoscopist via a rectangular box around the polyp on the colonoscope output feed.
Fig. 2.
Fig. 2.
Optical pathology algorithm of colon polyps showing an adenoma prediction (A) and serrated polyp prediction (B).
Fig. 3.
Fig. 3.
Optical pathology algorithm predicting an area of dysplasia within a segment of Barrett’s esophagus.

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Source: PubMed

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