Recent Advances in Endoscopic Ultrasound for Gallbladder Disease Diagnosis

• Published in: Diagnostics (Basel) Vol. 14; no. 4; p. 374
• Main Authors: Takahashi, Kosuke, Ozawa, Eisuke, Shimakura, Akane, Mori, Tomotaka, Miyaaki, Hisamitsu, Nakao, Kazuhiko
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.02.2024
• Subjects: Artificial intelligence; Cancer; Cholecystitis; Cholesterol; Diagnosis; Disease; Endoscopic ultrasonography; endoscopic ultrasound; Endoscopy; Gallbladder; gallbladder disease; Gallbladder diseases; Hyperplasia; lesions; Machine learning; Morphology; Polyps; Tumors; Ultrasonic imaging; gallbladder disease; lesions; endoscopic ultrasound; artificial intelligence
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14040374

Gallbladder (GB) disease is classified into two broad categories: GB wall-thickening and protuberant lesions, which include various lesions, such as adenomyomatosis, cholecystitis, GB polyps, and GB carcinoma. This review summarizes recent advances in the differential diagnosis of GB lesions, focusing primarily on endoscopic ultrasound (EUS) and related technologies. Fundamental B-mode EUS and contrast-enhanced harmonic EUS (CH-EUS) have been reported to be useful for the diagnosis of GB diseases because they can evaluate the thickening of the GB wall and protuberant lesions in detail. We also outline the current status of EUS-guided fine-needle aspiration (EUS-FNA) for GB lesions, as there have been scattered reports on EUS-FNA in recent years. Furthermore, artificial intelligence (AI) technologies, ranging from machine learning to deep learning, have become popular in healthcare for disease diagnosis, drug discovery, drug development, and patient risk identification. In this review, we outline the current status of AI in the diagnosis of GB.