Department of Neuro-oncology, Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
Department of Oral and Maxillofacial Surgery, University Medical Centre Utrecht, Utrecht, The Netherlands.
World Neurosurg. 2021 Feb;146:179-188. doi: 10.1016/j.wneu.2020.11.029. Epub 2020 Nov 13.
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.
背景:增强现实神经导航(ARN)系统可以在无需二维外部显示器的情况下叠加三维解剖结构和疾病。其准确性对于临床应用至关重要。我们对 ARN 系统报告的准确性进行了系统评价,并将其与传统红外神经导航(CIN)的准确性进行了比较。
方法:在 PubMed 和 Embase 中搜索 ARN 和 CIN 系统。对于 ARN,记录系统类型、患者到图像配准方法、准确性方法和系统准确性。对于 CIN,记录导航准确性,以目标注册误差(TRE)表示。对 ARN 和 CIN 系统的 TRE 进行了荟萃分析。
结果:共纳入 35 项研究,其中 12 项为 ARN 研究,23 项为 CIN 研究。ARN 系统可分为头戴式显示器和抬头式显示器。在 ARN 中,遇到了 4 种患者到图像配准方法,其中点对匹配是最常用的方法。描述了 5 种评估准确性的方法。比较了 94 项 ARN 系统的 TRE 测量值和 9058 项 CIN 系统的 TRE 测量值。ARN 系统的平均 TRE 为 2.5 毫米(95%置信区间,0.7-4.4),CIN 系统的平均 TRE 为 2.6 毫米(95%置信区间,2.1-3.1)。
结论:在 ARN 中,似乎缺乏评估准确性的最佳方法的共识。然而,ARN 系统似乎能够达到与 CIN 系统相当的准确性。未来的研究应该是前瞻性的,比较 TRE,并且应该以标准化的方式进行测量。
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