The traditional freehand technique for external ventricular drain (EVD) placement is most frequently used, but remains the primary risk factor for inaccurate drain placement. As this procedure could benefit from image guidance, the authors set forth to demonstrate the impact of augmented-reality (AR) assistance on the accuracy and learning curve of EVD placement compared with the freehand technique.

Sixteen medical students performed a total of 128 EVD placements on a custom-made phantom head, both before and after receiving a standardized training session. They were guided by either the freehand technique or by AR, which provided an anatomical overlay and tailored guidance for EVD placement through inside-out infrared tracking. The outcome was quantified by the metric accuracy of EVD placement as well as by its clinical quality.

The mean target error was significantly impacted by either AR (p = 0.003) or training (p = 0.02) in a direct comparison with the untrained freehand performance. Both untrained (11.9 ± 4.5 mm) and trained (12.2 ± 4.7 mm) AR performances were significantly better than the untrained freehand performance (19.9 ± 4.2 mm), which improved after training (13.5 ± 4.7 mm). The quality of EVD placement as assessed by the modified Kakarla scale (mKS) was significantly impacted by AR guidance (p = 0.005) but not by training (p = 0.07). Both untrained and trained AR performances (59.4% mKS grade 1 for both) were significantly better than the untrained freehand performance (25.0% mKS grade 1). Spatial aptitude testing revealed a correlation between perceptual ability and untrained AR-guided performance (r = 0.63).

Compared with the freehand technique, AR guidance for EVD placement yielded a higher outcome accuracy and quality for procedure novices. With AR, untrained individuals performed as well as trained individuals, which indicates that AR guidance not only improved performance but also positively impacted the learning curve. Future efforts will focus on the translation and evaluation of AR for EVD placement in the clinical setting.

最常使用的是传统的徒手技术放置心室外引流管 (EVD)，但仍然是引流管放置不准确的主要风险因素。由于此过程可以从图像引导中受益，因此作者阐述了与徒手技术相比，增强现实 (AR) 辅助对 EVD 放置的准确性和学习曲线的影响。

在接受标准化培训课程之前和之后，16 名医学生在定制的幻影头上总共进行了 128 次 EVD 放置。他们由徒手技术或 AR 引导，AR 通过由内向外红外跟踪为 EVD 放置提供解剖覆盖和量身定制的指导。结果通过 EVD 放置的度量精度及其临床质量进行量化。

在与未经训练的徒手表现的直接比较中，平均目标误差受到 AR (p = 0.003) 或训练 (p = 0.02) 的显着影响。未经训练 (11.9 ± 4.5 mm) 和训练 (12.2 ± 4.7 mm) 的 AR 性能均显着优于未经训练的徒手性能 (1​​9.9 ± 4.2 mm)，后者在训练后得到改善 (13.5 ± 4.7 mm)。AR 指导（p = 0.005）显着影响了由改良 Kakarla 量表（mKS）评估的 EVD 放置质量，但不受培训（p = 0.07）的影响。未经训练和受过训练的 AR 表现（两者均为 59.4% mKS 1 级）均显着优于未经训练的徒手表现（25.0% mKS 1 级）。空间能力测试揭示了感知能力与未经训练的 AR 引导性能之间的相关性 (r = 0.63)。

与手绘技术相比，用于 EVD 放置的 AR 指导为手术新手带来了更高的结果准确性和质量。使用 AR，未受过训练的个人表现与受过训练的个人一样好，这表明 AR 指导不仅提高了表现，而且对学习曲线产生了积极影响。未来的工作将集中在 AR 的翻译和评估上，以便在临床环境中放置 EVD。