The efficacy of 3D hologram support with mixed‐reality technique in pancreatobiliary endoscopy
Year: 2025
Type: article
Abstract: BACKGROUND AND AIMS: Mixed-reality (MR) technology is an advanced holographic imaging system that uses wearable devices to display three-dimensional (3D) images in clinical environments. In pancreatobiliary endoscopy, MR technology can facilitate 3D visualization of the bile and pancreatic ducts, enhancing spatial awareness and providing a more intuitive understanding of the anatomy. We evaluated the safety and efficacy of 3D hologram support (3D-HS) using MR in pancreatobiliary endoscopy. METHODS: This study included 30 patients who underwent pancreatobiliary endoscopy using 3D-HS between June 2023 and July 2024. The procedures included ERCP (n = 13) and interventional EUS (iEUS) (n = 17). The primary outcome was technical success, with secondary outcomes including total procedure-related time and adverse events. The conventional treatment group, matched for age and disease type, was compared to the 3D-HS group. a questionnaire was used to evaluate the MR device, 3D hologram images, and the overall evaluation with 3D-HS. RESULTS: The overall technical success rate was 96.7% (ERCP, 100%; iEUS, 93.8%). Adverse events occurred in 16.7% of cases; all were managed conservatively. comparison with conventional treatment showed no significant differences in outcomes, although conventional methods had shorter procedural times (p < 0.05). The questionnaire results indicated positive feedback on operability and image quality of 3D-HS, with enhanced anatomical understanding relative to 2D imaging, although some ERCP cases received lower ratings, particularly for intrahepatic bile duct stones. CONCLUSIONS: Pancreatobiliary endoscopy using 3D-HS can be safe and effective in enhancing anatomical understanding. Further studies are needed to optimize its use and reduce the total procedure time.
DOI: 10.1002/jhbp.12136
Institutions: Tokyo Medical University, Teikyo University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,700
OpenAlex ID: W4409751025
