NDLI: Feasibility and Accuracy of Thoracolumbar Pedicle Screw Placement Using an Augmented Reality Head Mounted Device

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Feasibility and Accuracy of Thoracolumbar Pedicle Screw Placement Using an Augmented Reality Head Mounted Device

Content Provider	MDPI
Author	Gustav, Burström Erik, Edström Frisk, Henrik Lindqvist, Eliza Persson, Oscar Weinzierl, Juliane Bruetzel, Linda K. Cewe, Paulina Elmi-Terander, Adrian
Copyright Year	2022
Description	Background: To investigate the accuracy of augmented reality (AR) navigation using the Magic Leap head mounted device (HMD), pedicle screws were minimally invasively placed in four spine phantoms. Methods: AR navigation provided by a combination of a conventional navigation system integrated with the Magic Leap head mounted device (AR-HMD) was used. Forty-eight screws were planned and inserted into Th11-L4 of the phantoms using the AR-HMD and navigated instruments. Postprocedural CT scans were used to grade the technical (deviation from the plan) and clinical (Gertzbein grade) accuracy of the screws. The time for each screw placement was recorded. Results: The mean deviation between navigation plan and screw position was 1.9 ± 0.7 mm (1.9 [0.3–4.1] mm) at the entry point and 1.4 ± 0.8 mm (1.2 [0.1–3.9] mm) at the screw tip. The angular deviation was 3.0 ± 1.4° (2.7 [0.4–6.2]°) and the mean time for screw placement was 130 ± 55 s (108 [58–437] s). The clinical accuracy was 94% according to the Gertzbein grading scale. Conclusion: The combination of an AR-HMD with a conventional navigation system for accurate minimally invasive screw placement is feasible and can exploit the benefits of AR in the perspective of the surgeon with the reliability of a conventional navigation system.
Starting Page	522
e-ISSN	14248220
DOI	10.3390/s22020522
Journal	Sensors
Issue Number	2
Volume Number	22
Language	English
Publisher	MDPI
Publisher Date	2022-01-11
Access Restriction	Open
Subject Keyword	Sensors Surgical Navigation Minimally Invasive Surgery Spine Surgery Augmented Reality Pedicle Screw Phantom
Content Type	Text
Resource Type	Article

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