Clinical prediction models for spine surgery applications are on the rise, with an increasing reliance on machine learning (ML) and deep learning (DL). Many of the predicted outcomes are uncommon; therefore, to ensure the models’ effectiveness in clinical practice it is crucial to properly evaluate them. This systematic review aims to identify and evaluate current research-based ML and DL models applied for spine surgery, specifically those predicting binary outcomes with a focus on their evaluation metrics. Overall, 60 papers were included, and the findings were reported according to the PRISMA guidelines. A total of 13 papers focused on lengths of stay (LOS), 12 on readmissions, 12 on non-home discharge, 6 on mortality, and 5 on reoperations. The target outcomes exhibited data imbalances ranging from 0.44% to 42.4%. A total of 59 papers reported the model’s area under the receiver operating characteristic (AUROC), 28 mentioned accuracies, 33 provided sensitivity, 29 discussed specificity, 28 addressed positive predictive value (PPV), 24 included the negative predictive value (NPV), 25 indicated the Brier score with 10 providing a null model Brier, and 8 detailed the F1 score. Additionally, data visualization varied among the included papers. This review discusses the use of appropriate evaluation schemes in ML and identifies several common errors and potential bias sources in the literature. Embracing these recommendations as the field advances may facilitate the integration of reliable and effective ML models in clinical settings.

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.

Artificial intelligence (AI) has the potential to augment clinicians' diagnostic and decision-making capabilities. It is well suited to identify patterns and correlations within data sets and may be applied to identify elements of importance in complex and data-laden areas such as patient selection, diagnostics, treatment, and outcome prediction. The development of modern neurosurgery has been dependent on major technological advances. In line with this, a growing interest is seen in the use of AI to assist in neurosurgical research and enhance neurosurgical practices. A bibliometric analysis of the 50 most-cited articles alluding to the use of AI in neurosurgery, from inception until July of 2022, was undertaken using the Web of Science database. Statistical analyses were performed on R. The citation count ranged from 29 to 159 (mean: 51.9, standard deviation: 24.8), and the top-cited article was a 2018 systematic review published in World Neurosurgery. Most articles were published after 2015 (85%). The United States was the largest contributing country on the list with 22 articles. Four first and last authors, each, had 2 or more publications. Female first and last authorship was attributed to 18% and 0% of the articles, respectively. This review highlights the most-impactful articles pertaining to AI in the field of neurosurgery. Although female authors were significantly underrepresented on the list, their work was at least as impactful as their male peers. Finally, the striking dominance of articles originating from the developed world raises concerns as to the future of AI in attending to the global health crisis.

Background: Hybrid operating rooms (hybrid-ORs) combine the functionalities of a conventional surgical theater with the advanced imaging technologies of a radiological suite. Hybrid-ORs are usually equipped with CBCT devices providing both 2D and 3D imaging capability that can be used for both interventional radiology and image guided surgical applications. Across all fields of surgery, the use of hybrid-ORs is gaining in traction, and neurosurgery is no exception. We hence aimed to comprehensively review the use of hybrid-ORs, the associated advantages, and disadvantages specific to the field of neurosurgery. Materials and methods: Electronic databases were searched for all studies on hybrid-ORs from inception to May 2022. Findings of matching studies were pooled to strengthen the current body of evidence. Results: Seventy-four studies were included in this review. Hybrid-ORs were mainly used in endovascular surgery (n = 41) and spine surgery (n = 33). Navigation systems were the most common additional technology employed along with the CBCT systems in the hybrid-ORs. Reported advantages of hybrid-ORs included immediate assessment of outcomes, reduced surgical revision rate, and the ability to perform combined open and endovascular procedures, among others. Concerns about increased radiation exposure and procedural time were some of the limitations mentioned. Conclusion: In the field of neurosurgery, the use of hybrid-ORs for different applications is increasing. Hybrid-ORs provide preprocedure, intraprocedure, and end-of-procedure imaging capabilities, thereby increasing surgical precision, and reducing the need for postoperative imaging and correction surgeries. Despite these advantages, radiation exposure to patient and staff is an important concern.

Background and objectives Spontaneous spinal cord infarction (SCInf) is a rare condition resulting in acute neurological impairment. Consensus on diagnostic criteria is lacking, which may present a challenge for the physician. This review aims to analyse the current literature on spontaneous SCInf, focusing on epidemiology, the diagnostic process, treatment strategies and neurological outcomes. Methods The study was performed in accordance with a previously published protocol. PubMed, Web of Science and Embase were searched using the keywords ‘spontaneous’, ‘spinal cord’, ‘infarction’ and ‘ischaemic’. The eligibility of studies was evaluated in two steps by multiple reviewers. Data from eligible studies were extracted and systematically analysed. Results 440 patients from 33 studies were included in this systematic review. Analysis of vascular risk factors showed that hypertension was present in 40%, followed by smoking in 30%, dyslipidaemia in 29% and diabetes in 16%. The severity of symptoms at admission according to the American Spinal Injury Association (ASIA) Impairment Scale was score A 19%, score B14%, score C36% and score D32%. The mean follow-up period was 34.8 (±12.2) months. ASIA score at follow-up showed score A 11%, score B 3%, score C 16%, score D 67% and score E 2%. The overall mortality during the follow-up period was 5%. When used, MRI with diffusion-weighted imaging (DWI) supported the diagnosis in 81% of cases. At follow-up, 71% of the patients were able to walk with or without walking aids. Conclusion The findings suggest a significant role for vascular risk factors in the pathophysiology of spontaneous SCInf. In the diagnostic workup, the use of DWI along with an MRI may help in confirming the diagnosis. The findings at follow-up suggest that neurological recovery is to be expected, with the majority of patients regaining ambulation. This systematic review highlights gaps in the literature and underscores the necessity for further research to establish diagnostic criteria and treatment guidelines.

Background: Spinal meningiomas are the most common primary intradural spinal tumors. Although they are a separate entity, a large portion of the knowledge on spinal meningiomas is based on findings in intracranial meningiomas. Therefore, a comprehensive review of all the literature on spinal meningiomas was performed. Methods: Electronic databases were searched for all studies on spinal meningiomas dating from 2000 and onward. Findings of matching studies were pooled to strengthen the current body of evidence. Results: A total of 104 studies were included. The majority of patients were female (72.83%), elderly (peak decade: seventh), and had a world health organization (WHO) grade 1 tumor (95.7%). Interestingly, the minority of pediatric patients had a male overrepresentation (62.0% vs. 27.17%) and higher-grade tumors (33.3% vs. 4.3%). Sensory and motor dysfunction and pain were the most common presenting symptoms. Despite a handful of studies reporting promising findings associated with the use of non-surgical treatment options, the literature still suffers from contradictory results and limitations of study designs. Conclusions: Elderly females with WHO grade 1 tumors constituted the stereotypical type of patient. Compared to surgical alternatives, the evidence for the use of non-surgical treatments is still relatively weak.