OBJECTIVE: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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.

Background: Before starting surgery for the resection of an intracranial tumor, its outlines are typically marked on the skin of the patient. This allows for the planning of the optimal skin incision, craniotomy, and angle of approach. Conventionally, the surgeon determines tumor borders using neuronavigation with a tracked pointer. However, interpretation errors can lead to important deviations, especially for deep-seated tumors, potentially resulting in a suboptimal approach with incomplete exposure. Augmented reality (AR) allows displaying of the tumor and critical structures directly on the patient, which can simplify and improve surgical preparation.Methods: We developed an AR-based workflow for intracranial tumor resection planning deployed on the Microsoft HoloLens II, which exploits the built-in infrared-camera for tracking the patient. We initially performed a phantom study to assess the accuracy of the registration and tracking. Following this, we evaluated the AR-based planning step in a prospective clinical study for patients undergoing resection of a brain tumor. This planning step was performed by 12 surgeons and trainees with varying degrees of experience. After patient registration, tumor outlines were marked on the patient's skin by different investigators, consecutively using a conventional neuronavigation system and an AR-based system. Their performance in both registration and delineation was measured in terms of accuracy and duration and compared.Results: During phantom testing, registration errors remained below 2.0 mm and 2.0° for both AR-based navigation and conventional neuronavigation, with no significant difference between both systems. In the prospective clinical trial, 20 patients underwent tumor resection planning. Registration accuracy was independent of user experience for both AR-based navigation and the commercial neuronavigation system. AR-guided tumor delineation was deemed superior in 65% of cases, equally good in 30% of cases, and inferior in 5% of cases when compared to the conventional navigation system. The overall planning time (AR = 119 ± 44 s, conventional = 187 ± 56 s) was significantly reduced through the adoption of the AR workflow (p < 0.001), with an average time reduction of 39%.Conclusion: By providing a more intuitive visualization of relevant data to the surgeon, AR navigation provides an accurate method for tumor resection planning that is quicker and more intuitive than conventional neuronavigation. Further research should focus on intraoperative implementations.

Extent of resection (EOR) is a well-known prognostic factor in patients with newly diagnosed IDH-wildtype glioblastoma. However, reported survival times across resection categories vary between reports, and outcomes of submaximal or supramaximal resection remain less well defined. We conducted a systematic review and meta-analysis on the association between EOR and overall survival (OS) in patients with newly diagnosed IDH-wildtype glioblastoma treated with chemoradiotherapy. Studies were included if OS was reported by EOR category. Risk ratios (RRs) for 1- and 2-year survival were pooled using a random-effects model. Study quality was assessed using the Newcastle-Ottawa Scale. Thirty-one studies involving 26,167 patients were included. Supramaximal resection (SupraMR) was associated with significantly improved 2-year survival compared to maximal CE resection (MR) (RR 0.70, 95 % CI 0.55–0.88). Compared to submaximal resection (subMR), MR was associated with higher 1-year survival (RR 0.59, 95 % CI 0.53–0.67) and 2-year survival (RR 0.82, 95 % CI 0.77–0.87). Biopsy alone was associated with the poorest outcome. Findings remained robust in sensitivity analyses excluding SEER and RTOG cohorts. Increasing EOR seems to be associated with improved survival in newly diagnosed IDH-wildtype glioblastoma. SupraMR offers the greatest benefit, while submaximal resection appears to be more favorable than biopsy. These findings support the prognostic relevance of EOR and underscore the need for prospective studies with standardized resection classifications. The balanced summary of survival data for each resection class provided in this review can serve as a basis for effect estimation and sample size calculations in future trials.

With increasing use of robotic surgical adjuncts, artificial intelligence and augmented reality in neurosurgery, the automated analysis of digital images and videos acquired over various procedures becomes a subject of increased interest. While several computer vision (CV) methods have been developed and implemented for analyzing surgical scenes, few studies have been dedicated to neurosurgery. In this work, we present a systematic literature review focusing on CV methodologies specifically applied to the analysis of neurosurgical procedures based on intra-operative images and videos. Additionally, we provide recommendations for the future developments of CV models in neurosurgery. We conducted a systematic literature search in multiple databases until January 17, 2023, including Web of Science, PubMed, IEEE Xplore, Embase, and SpringerLink. We identified 17 studies employing CV algorithms on neurosurgical videos/images. The most common applications of CV were tool and neuroanatomical structure detection or characterization, and to a lesser extent, surgical workflow analysis. Convolutional neural networks (CNN) were the most frequently utilized architecture for CV models (65%), demonstrating superior performances in tool detection and segmentation. In particular, mask recurrent-CNN manifested most robust performance outcomes across different modalities. Our systematic review demonstrates that CV models have been reported that can effectively detect and differentiate tools, surgical phases, neuroanatomical structures, as well as critical events in complex neurosurgical scenes with accuracies above 95%. Automated tool recognition contributes to objective characterization and assessment of surgical performance, with potential applications in neurosurgical training and intra-operative safety management.

In several orthopedic procedures, the accurate use of surgical power tools is critical to avoid damage to surrounding tissues. As such, various guidance techniques and safety measures were developed. Augmented reality (AR) guidance shows promise but requires validation. We evaluated a new approach using an inside-out infrared tracking solution for the HoloLens to compensate for its limited tracking performance. Eighteen participants with varying levels of experience (student, trainee, expert) each drilled twelve trajectories (six perpendicular, six oblique) in equidimensional wooden logs. Three different techniques were evaluated: freehand drilling; proprioception-guided drilling towards the contralateral index finger; and AR-guided drilling using a tracked drill and a virtual overlay of the log with predefined guidance vectors. The angular errors between planned and performed trajectories were compared using a mixed-design ANOVA. The results demonstrated that guidance technique (p < 0.001) and drilling direction (p < 0.001) significantly affected drilling accuracy, while experience (p = 0.75) did not. AR outperformed both other techniques, particularly for oblique trajectories (p < 0.001). For perpendicular trajectories, it only outperformed proprioception guidance (p = 0.04). Target plots revealed an important scatter perpendicular to the longitudinal axis of the log during freehand and proprioception-guided drilling, especially for oblique trajectories. This inaccuracy disappeared during AR-guided drilling. As such, we were able to conclude that AR guidance using inside-out infrared tracking reduced angular uncertainty during directional drilling, resulting in improved drilling accuracy. This improvement was particularly noticeable for complex trajectories and angles. The benefits of AR guidance were observed across all experience levels, highlighting its potential for orthopedic applications. We believe this study opens the way for the methodical evaluation of AR guidance in specific orthopedic use cases.