Surgical simulation practices have witnessed a rapid expansion as an invaluable approach to resident training in recent years. One emerging way of implementing simulation is the adoption of extended reality (XR) technologies, which enable trainees to hone their skills by allowing interaction with virtual 3D objects placed in either real-world imagery or virtual environments. The goal of the present systematic review is to survey and broach the topic of XR in neurosurgery, with a focus on education. Five databases were investigated, leading to the inclusion of 31 studies after a thorough reviewing process. Focusing on user performance (UP) and user experience (UX), the body of evidence provided by these 31 studies showed that this technology has, in fact, the potential of enhancing neurosurgical education through the use of a wide array of both objective and subjective metrics. Recent research on the topic has so far produced solid results, particularly showing improvements in young residents, compared to other groups and over time. In conclusion, this review not only aids to a better understanding of the use of XR in neurosurgical education, but also highlights the areas where further research is entailed while also providing valuable insight into future applications.

Purpose: This bibliometric analysis of the top 100 cited articles on extended reality (XR) in neurosurgery aimed to reveal trends in this research field. Gender differences in authorship and global distribution of the most-cited articles were also addressed. Methods: A Web of Science electronic database search was conducted. The top 100 most-cited articles related to the scope of this review were retrieved and analyzed for trends in publications, journal characteristics, authorship, global distribution, study design, and focus areas. After a brief description of the top 100 publications, a comparative analysis between spinal and cranial publications was performed. Results: From 2005, there was a significant increase in spinal neurosurgery publications with a focus on pedicle screw placement. Most articles were original research studies, with an emphasis on augmented reality (AR). In cranial neurosurgery, there was no notable increase in publications. There was an increase in studies assessing both AR and virtual reality (VR) research, with a notable emphasis on VR compared to AR. Education, surgical skills assessment, and surgical planning were more common themes in cranial studies compared to spinal studies. Female authorship was notably low in both groups, with no significant increase over time. The USA and Canada contributed most of the publications in the research field. Conclusions: Research regarding the use of XR in neurosurgery increased significantly from 2005. Cranial research focused on VR and resident education while spinal research focused on AR and neuronavigation. Female authorship was underrepresented. North America provides most of the high-impact research in this area.

Background: In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. Methods: This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. Results: In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. Conclusions: This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue.

Introduction Nerve sheath tumours arise from both the central and peripheral nervous systems. In particular, cases of spinal or paraspinal origins are scarce and poorly covered in the literature. This systematic review aims to summarise the body of evidence regarding spinal nerve sheath tumours and assess its quality, to provide the current knowledge on epidemiology, tumour characteristics, diagnostics, treatment strategies and outcomes. Methods and analysis Databases including PubMed, Web of Science and Embase will be searched using keywords such as “spinal”, “nerve sheath”, “neurofibroma”, “schwannoma”, “neurinoma” and “neurilemoma”. The search will be limited to studies published no earlier than 2000 without language restrictions. Case reports, editorials, letters and reviews will be excluded. Reference lists of identified studies will be searched to find possible additional relevant records. Identified studies will be screened for inclusion, by one reviewer at first and then two independent ones in the next step to increase the external validity. The Rayyan platform will be used for the screening and inclusion process. Data extraction within several predetermined areas of interest will proceed. Subjects of interest include epidemiology, histopathology, radiological diagnostics, surgery, complications, non-surgical treatment alternatives, disease outcomes and predictors of outcome, and recurrence rates. On satisfactory amount of homogenous data, a meta-analysis of key outcomes such as recurrence risk or postoperative neurological improvement will be performed. This systematic review will primarily serve as a reference guide to aid in diagnosis and treatment of patients with spinal schwannomas, while also spotlighting the knowledge gaps in the literature to help guide future research initiatives. Ethics and dissemination Ethics approval is not required for the protocol or review as both are based on existing publications. For dissemination, the final manuscript will be submitted to a peer-reviewed journal.

Purpose: Simple generalized thresholds for assessing functional impairment in clinical testing are limited, as they fail to consider patient-specific properties such as age, body height, and body mass index. A previously developed machine learning-based model for personalized testing using the five-repetition sit-to-stand (5R-STS) test, that estimates personalized upper limits of normal (ULN) to identify objective functional impairment (OFI) was externally validated to evaluate its performance and generalizability across cohorts. Methods: Only healthy individuals were included in the study. After the machine learning-based model was applied to this external dataset, expected and observed 5R-STS test times were compared using standardized performance assessment metrics including root mean square error (RMSE), mean absolute error (MAE), and R ² values. Additionally, a Bland–Altman analysis was performed to assess agreement between observed and expected values. Validation of the expected ULN involved comparing the proportion of individuals exceeding their personalized thresholds with the corresponding proportion based on the generalized threshold. Subgroup analyses by test setting and country of residence were additionally performed, along with a graphical assessment of model performance. Results: Application of the model to 171 healthy individuals resulted in an RMSE of 2.33 (95% CI: 1.93 to 2.73) seconds, MAE of 1.70 (95% CI: 1.47 to 1.94) seconds, and R ² of 0.064 (95% CI: -0.25 to 0.15). The Bland–Altman analysis demonstrated a mean bias of -1.1 s. Based on the personalized ULNs, OFI was classified in 17.5% of individuals, compared to 6.4% when using the generalized threshold of 10.4 s. These analyses indicated limited external generalization, with acceptable approximation for some faster and mid-range test times but systematic underestimation of slower test times. Multivariable regression analyses showed that remote testing was independently associated with greater prediction bias and higher odds of personalized ULN exceedance compared with supervised testing (adjusted mean difference: − 0.90 s; adjusted OR: 10.7). Exploratory country-of-residence analyses showed differences across the three largest national subgroups. 130 (76.1%) rated ease of use as excellent, and 145 (84.9%) rated clarity of instructions as excellent. 143 participants (83.6%) indicated that they prefer the 5R-STS over a battery of questionnaires. Conclusions: In the context of personalized testing, moving toward individually focused precision assessment of patients requires rigorous external validation to ensure the robustness of such applied computational methods. In this external validation, the model demonstrated limited generalization including a systematic underestimation of slower test times and insufficient personalized ULN calibration. These findings indicate that external validity of models derived from single-center data can be limited, underscoring the importance of comprehensive external validation and, potentially, multicenter retraining before clinical implementation.