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Artif Intell Gastroenterol. Apr 28, 2022; 3(2): 54-65
Published online Apr 28, 2022. doi: 10.35712/aig.v3.i2.54
Machine learning in endoscopic ultrasonography and the pancreas: The new frontier?
Cem Simsek, Linda S Lee
Cem Simsek, Department of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, United States
Linda S Lee, Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
Author contributions: Simsek C collected data and wrote the paper; Lee L carried out data collection; both authors read, edited, and approved the final manuscript.
Conflict-of-interest statement: Cem Simsek is co-founder of Algomedicus Inc.
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: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Linda S Lee, MD, Associate Professor, Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, United States. lslee@partners.org
Received: February 1, 2022
Peer-review started: February 1, 2022
First decision: February 18, 2022
Revised: March 28, 2022
Accepted: April 19, 2022
Article in press: April 19, 2022
Published online: April 28, 2022
Processing time: 87 Days and 8.9 Hours
Abstract

Pancreatic diseases have a substantial burden on society which is predicted to increase further over the next decades. Endoscopic ultrasonography (EUS) remains the best available diagnostic method to assess the pancreas, however, there remains room for improvement. Artificial intelligence (AI) approaches have been adopted to assess pancreatic diseases for over a decade, but this methodology has recently reached a new era with the innovative machine learning algorithms which can process, recognize, and label endosonographic images. Our review provides a targeted summary of AI in EUS for pancreatic diseases. Included studies cover a wide spectrum of pancreatic diseases from pancreatic cystic lesions to pancreatic masses and diagnosis of pancreatic cancer, chronic pancreatitis, and autoimmune pancreatitis. For these, AI models seemed highly successful, although the results should be evaluated carefully as the tasks, datasets and models were greatly heterogenous. In addition to use in diagnostics, AI was also tested as a procedural real-time assistant for EUS-guided biopsy as well as recognition of standard pancreatic stations and labeling anatomical landmarks during routine examination. Studies thus far have suggested that the adoption of AI in pancreatic EUS is highly promising and further opportunities should be explored in the field.

Keywords: Artificial intelligence; Pancreas; Endoscopic ultrasonography; Pancreatic cancer; Autoimmune pancreatitis; Pancreatic cystic lesions

Core Tip: Several reviews in the literature have discussed the use of artificial intelligence in pancreatic disease. However, this is the first review that focuses on the application of artificial intelligence (AI) specifically to endoscopic ultrasonography (EUS) of the pancreas, including pancreatic cystic lesions, pancreatic cancer, chronic pancreatitis, and autoimmune pancreatitis, where it appears to enhance EUS diagnosis. AI may also offer real-time assistance during procedures to direct biopsy towards the highest yield areas as well augment EUS training.