Related Experiment Video
Updated: Sep 13, 2025

Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model
Published on: May 24, 2024
Augmented Reality in Spine Surgery: A Narrative Review of Clinical Accuracy, Workflow Efficiency, and Barriers to
Ahmed Nadeem-Tariq1, Sarah Kazemeini2, Pratiksha Kaur3
1Otolaryngology - Head and Neck Surgery, University of Nevada, Las Vegas (UNLV) School of Medicine, Las Vegas, USA.
Abstract:
Augmented reality (AR) has emerged as a promising intraoperative navigation tool in spine surgery, particularly for pedicle screw placement, which demands high spatial precision and carries risks of neurovascular injury. AR platforms offer real-time overlays of patient anatomy, aiming to enhance accuracy, reduce radiation exposure, and streamline operative workflow. This narrative review synthesizes the early clinical experiences, efficacy data, and barriers to adoption for AR-assisted navigation in spine surgery. It also explores the role of AR in surgical training, workflow optimization, and future integration into routine practice. A comprehensive search of PubMed and ScienceDirect was conducted for English-language studies from January 2004 to May 2025. Inclusion criteria focused on peer-reviewed trials evaluating AR in spinal procedures, with outcomes including screw placement accuracy, radiation exposure, operative time, and user experience. Across multiple platforms, including Microsoft HoloLens and Augmedics xVision, AR-assisted navigation demonstrated pedicle screw placement accuracy rates ranging from 93% to 100%, often with reduced fluoroscopy time and improved surgeon ergonomics. Preliminary data suggest utility in complex deformity surgeries and surgical training. However, widespread adoption remains limited by cost, integration challenges, and a lack of large-scale, multicenter trials. AR represents a viable adjunct to current navigation technologies in spine surgery, showing early promise in enhancing precision and efficiency. Broader adoption will require improved validation, standardization of training protocols, and cost-effectiveness data.

