Current Accuracy of Augmented Reality Neuronavigation Systems: Systematic Review and Meta-Analysis

Abstract

Background: Augmented reality neuronavigation (ARN) systems can overlay three-dimensional anatomy and disease 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).

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 were 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: Thirty-five studies were included, 12 for ARN and 23 for CIN. ARN systems could be divided into head-mounted display and heads-up display. In ARN, 4 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. Ninety-four TRE measurements of ARN systems were compared with 9058 TRE measurements of CIN systems. Mean TRE was 2.5 mm (95% confidence interval, 0.7-4.4) for ARN systems and 2.6 mm (95% confidence interval, 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 to CIN systems. Future studies should be prospective and compare TREs, which should be measured in a standardized fashion.

Keywords: Accuracy; Augmented reality; Neuronavigation.