Year: 2025
Type: article
Study design: Observational Study
Abstract: Objective: This study aims to (1) develop an augmented reality (AR) navigation platform for craniomaxillofacial (CMF) and head and neck surgery; (2) apply it to a range of surgical cases; and (3) evaluate the advantages, disadvantages, and clinical opportunities for AR navigation. Study Design: A multi-center retrospective case series. Setting: Four tertiary care academic centers. Methods: A novel AR navigation platform was collaboratively developed with Xironetic and deployed intraoperatively using only a head-mounted display (Microsoft HoloLens 2). Virtual surgical plans were generated from computed tomography/magnetic resonance imaging data and uploaded onto the AR platform. A reference array was mounted to the patient, and the virtual plan was registered to the patient intraoperatively. A retrospective review of all AR-navigated CMF cases since September 2023 was performed. Results: Thirty-three cases were reviewed and classified as either trauma, orthognathic, tumor, or craniofacial. The AR platform had several advantages over traditional navigation including real-time 3D visualization of the surgical plan, identification of critical structures, and real-time tracking. Furthermore, this case series presents the first-known examples of (1) AR instrument tracking for midface osteotomies, (2) AR tracking of the zygomaticomaxillary complex during fracture reduction, (3) mandibular tracking in orthognathic surgery, (4) AR fibula cutting guides for mandibular reconstruction, and (5) integration of real-time infrared visualization in an AR headset for vasculature identification. Conclusion: While still a developing technology, AR navigation provides several advantages over traditional navigation for CMF and head and neck surgery, including heads up, interactive 3D visualization of the surgical plan, identification of critical anatomy, and real-time tracking.
Source: OTO Open
DOI: 10.1002/oto2.70108
Authors: E. Brandon Strong, E. Brandon Strong, Anuj Patel, Alexander P. Marston, Cameron Sadegh, Jeffrey Potts, Darin Johnston, David Ahn, Shae Bryant, Michael Li, Osama Raslan, Steven A. Lucero, Marc J. Fischer, Marike Zwienenberg, Neha Sharma, Florian Thieringer, Christian El Amm, Kiarash Shahlaie, Marc Metzger, E. Bradley Strong, E. Bradley Strong
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,430
OpenAlex ID: W4409355715
