Assessing the Accuracy of Spinal Instrumentation Using Augmented Reality (AR): A Systematic Review of the Literature and Meta Analysis

Wednesday, February 21, 2024

Presenting Author(s)

Tej D. Azad, MD, MS

Resident
Department of Neurosurgery, The Johns Hopkins University School of Medicine
Baltimore, MD, US

Co-Author(s)

AB

Ali Bydon, MD

Professor
Johns Hopkins University
Baltimore, Maryland, United States
Brendan F. Judy, MD

Resident
Johns Hopkins Department of Neurosurgery
Baltimore, Maryland, United States
JK

Jawad Khalifeh, MD

Resident
Johns Hopkins Department of Neurosurgery
Baltimore, Maryland, United States
CL

Connor Liu, MD

Resident
Johns Hopkins Department of Neurosurgery
Baltimore, Maryland, United States
JL

Jiaqi Liu, BS

Medical Student
Georgetown University School of Medicine, United States
Bhavya Pahwa, n/a

Medical Student
University College of Medical Sciences and GTB Hospital
Amritsar, India
KR

Kathleen R. Ran, AB (she/her/hers)

Medical Student
Johns Hopkins University School of Medicine
Baltimore, Maryland, United States
Shahab Aldin Sattari, MD (he/him/his)

Postdoc Research Fellow
Johns Hopkins
Jovanna A. Tracz, n/a

Medical Student Researcher
Johns Hopkins Department of Neurosurgery
Norfolk, Virginia, United States
TW

Timothy Witham, MD

Professor
Johns Hopkins University
Towson, Maryland, United States

Introduction: Technological advancements, particularly in the realm of augmented reality (AR), may facilitate more accurate and precise pedicle screw placement. AR integrates virtual data into the operator’s real-world view, allowing for visualization of patient specific anatomy and navigated trajectories. We aimed to conduct a meta-analysis of the accuracy of the pedicle screw placement using AR-based systems.

Methods: A systematic review of the literature and meta-analysis was performed using the PubMed/MEDLINE database, including studies reporting accuracy of pedicle screw placement using AR.

Results: Eight studies with 163 patients and 1,259 screws were included in the analysis. X-Vision (XVS) was the most commonly used AR system (595 screws) followed by Allura AR surgical navigation system (ARSN) (462 screws). The overall accuracy was calculated as 97.2% (95% CI 96.2%-98.1% p< 0.001). Subgroup analysis revealed that there was no statistically significant difference in the accuracy rates achieved by XVS and Allura ARSN (p=0.092).

Conclusion : AR enables reliable, accurate placement of spinal instrumentation. Future research efforts should focus on comparative studies, cost-effectiveness, operative time, and radiation exposure.