Review World Neurosurg 2020 Nov 13;S1878-8750(20)32404-9. doi: 10.1016/j.wneu.2020.11.029.
Current Accuracy of Augmented Reality Neuronavigation Systems: Systematic Review and Meta-Analysis
T Fick 1, J A M van Doormaal 2, E W Hoving 3, P W A Willems 4, T P C van Doormaal 5Affiliations expand
- PMID: 33197631
- DOI: 10.1016/j.wneu.2020.11.029
Abstract
Background: Augmented reality neuronavigation (ARN) systems can overlay three-dimensional anatomy and pathology without the need for a two-dimensional external monitor. Accuracy is crucial for their clinical applicability. We performed a systematic review regarding the reported accuracy of ARN systems and compared them with the accuracy of conventional infrared neuronavigation (CIN).
Objective: Explore the current navigation accuracy of ARN systems and compare them with CIN.
Methods: Pubmed and Embase were searched for ARN and CIN systems. For ARN: type of system, method of patient-to-image registration, accuracy method and accuracy of the system was noted. For CIN: navigation accuracy, expressed as target registration error (TRE), was noted. A meta-analysis was performed comparing the TRE of ARN and CIN systems.
Results: 35 studies were included, 12 for ARN and 23 for CIN. ARN systems were divided into head-mounted display and heads-up display. In ARN, four methods were encountered for patient-to-image registration, of which point-pair matching was the one most frequently used. Five methods for assessing accuracy were described. 94 TRE measurements of ARN systems were compared with 9058 TRE measurements of CIN systems. Mean TRE was 2.5 mm (CI 95% 0.7 – 4.4) for ARN systems and 2.6 mm (CI 95% 2.1 – 3.1) for CIN systems.
Conclusions: In ARN, there seems to be lack of agreement regarding the best method to assess accuracy. Nevertheless, ARN systems seem able to achieve an accuracy comparable with CIN systems. Future studies should be prospective and compare TREs which should be measured in a standardized fashion.
Keywords: Accuracy; Augmented Reality; Neuronavigation.
Copyright © 2020 Elsevier Inc. All rights reserved.
Similar articles
- Properties of the target registration error for surface matching in neuronavigation.Wang MN, Song ZJ.Comput Aided Surg. 2011;16(4):161-9. doi: 10.3109/10929088.2011.579791. Epub 2011 Jun 1.PMID: 21631164
- Localization and registration accuracy in image guided neurosurgery: a clinical study.Shamir RR, Joskowicz L, Spektor S, Shoshan Y.Int J Comput Assist Radiol Surg. 2009 Jan;4(1):45-52. doi: 10.1007/s11548-008-0268-8. Epub 2008 Oct 28.PMID: 20033601 Clinical Trial.
- Fusion of augmented reality imaging with the endoscopic view for endonasal skull base surgery; a novel application for surgical navigation based on intraoperative cone beam computed tomography and optical tracking.Lai M, Skyrman S, Shan C, Babic D, Homan R, Edström E, Persson O, Burström G, Elmi-Terander A, Hendriks BHW, de With PHN.PLoS One. 2020 Jan 16;15(1):e0227312. doi: 10.1371/journal.pone.0227312. eCollection 2020.PMID: 31945082 Free PMC article.
- Augmented reality in neurosurgery: a systematic review.Meola A, Cutolo F, Carbone M, Cagnazzo F, Ferrari M, Ferrari V.Neurosurg Rev. 2017 Oct;40(4):537-548. doi: 10.1007/s10143-016-0732-9. Epub 2016 May 7.PMID: 27154018 Free PMC article. Review.
- Accuracy of Pedicle Screw Insertion Among 3 Image-Guided Navigation Systems: Systematic Review and Meta-Analysis.Du JP, Fan Y, Wu QN, Wang DH, Zhang J, Hao DJ.World Neurosurg. 2018 Jan;109:24-30. doi: 10.1016/j.wneu.2017.07.154. Epub 2017 Sep 13.PMID: 28917704 Review.