Artificial intelligence implementation in pancreaticobiliary endoscopy

ABSTRACT

Introduction

Artificial intelligence has been rapidly deployed in gastroenterology and endoscopy. The acceleration of deep convolutional neural networks along with hardware development has allowed implementation of artificial intelligence algorithms into real-time endoscopy, particularly colonoscopy. However, artificial intelligence implementation in pancreaticobiliary endoscopy is nascent.

Areas Covered

Initial studies have been conducted in endoscopic retrograde pancreatography (ERCP), endoscopic ultrasound (EUS), and digital single operator cholangioscopy (DSOC). Machine learning has been implemented in identifying significant landmarks, including the ampulla on ERCP, and the bile duct, pancreas, and portal confluence on EUS. Moreover, artificial intelligence algorithms have been deployed in differentiating pathology including pancreas cancer, autoimmune pancreatitis, pancreatic cystic lesions, and biliary strictures.

Expert Opinion

There have been relatively few studies with limited sample sizes in developing these machine learning algorithms. Despite the early successful demonstration of artificial intelligence in pancreaticobiliary endoscopy, additional research needs to be conducted with larger data sets to improve generalizability and assessed in real-time endoscopy before clinical implementation. However, pancreaticobiliary endoscopy remains a promising avenue of artificial intelligence application with the potential to improve clinical practice and outcomes.

KEYWORDS:

• Artificial intelligence
• computer vision
• Endoscopy
• ERCP
• EUS

Article highlights

• There has been a growing interest in implementing artificial intelligence into pancreaticobiliary endoscopy.

• Artificial intelligence has been applied to ERCP (endoscopic retrograde cholangiopancreatography) and EUS (endoscopic ultrasound) in anatomic landmarking including the ampulla, bile duct, pancreas, and portal confluence.

• Convolutional neural networks have been implemented into EUS and DSOC (direct single operator cholangioscopy) to assist in diagnosis of autoimmune pancreatitis, pancreas cancer, biliary strictures, and pancreatic cystic neoplasms.

• Additional studies are required in real-time ERCP and EUS before full clinical implementation.

• The potential use cases of artificial intelligence in ERCP and EUS have promise to improve clinical outcomes.

Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.