Department of Medicine, Federal University of Sergipe, Aracaju, Sergipe, Brazil.
Health Sciences Graduate Program, Federal University of Sergipe, Aracaju, Sergipe, Brazil; Department of Neurosurgery, Fundação de Beneficência Hospital de Cirurgia, Aracaju, Sergipe, Brazil.
World Neurosurg. 2022 Nov;167:e1261-e1267. doi: 10.1016/j.wneu.2022.09.014. Epub 2022 Sep 8.
Image-guided surgery has shown great utility in neurosurgery, especially in allowing for more accurate surgical planning and navigation. The current gold standard for image-guided neurosurgery is neuronavigation, which provides millimetric accuracy on such tasks. However, these approaches often require a complicated setup and have high cost, hindering their potential in low- and middle-income countries. The aim of this study was to develop and evaluate the performance of a mobile-based augmented reality neuronavigation solution under different conditions in a preclinical environment.
The application was developed using the Swift programming language and was tested on a replica of a human scalp under variable lighting, with different numbers of registration points and target point position conditions. For each condition, reference points were input into the application, and the target points were computed for 10 iterations. The mean registration error and target error were used to assess the performance of the application.
In the best-case scenario, the proposed solution had a mean target error of 2.6 ± 1.6 mm.
Our approach provides a viable, low-cost, easy-to-use, portable method for locating points on the scalp surface with an accuracy of 2.6 ± 1.6 mm in the best-case scenario.
影像引导手术在神经外科中显示出了巨大的应用价值，尤其是在实现更精确的手术规划和导航方面。目前神经外科影像引导的金标准是神经导航，它可以在这些任务中提供毫米级的精度。然而，这些方法通常需要复杂的设置，成本高昂，限制了它们在中低收入国家的应用潜力。本研究旨在开发并评估一种基于移动设备的增强现实神经导航解决方案在不同条件下的性能，这些条件在临床前环境中进行测试。
该应用程序使用 Swift 编程语言开发，并在模拟的人类头皮下进行测试，测试条件包括不同的光照条件、不同数量的配准点和目标点位置条件。对于每种条件，将参考点输入到应用程序中，并对 10 次迭代进行目标点计算。平均配准误差和目标误差用于评估应用程序的性能。
在最佳情况下，所提出的解决方案的平均目标误差为 2.6±1.6 毫米。
我们的方法提供了一种可行的、低成本、易于使用、便携式的方法，可以在最佳情况下以 2.6±1.6 毫米的精度定位头皮表面上的点。
