OBJECTIVE: The authors, who are from Indonesia, Japan, Malaysia, the Philippines, and Taiwan, sought to illustrate the processes of training neurosurgeons in their respective settings by presenting data and analyses of the current state of neurosurgical education across the East Asian region. METHODS: The authors obtained quantitative data as key indicators of the neurosurgical workforce from each country. Qualitative data analysis was also done to provide a description of the current state of neurosurgical training and education in the region. A strengths, weaknesses, opportunities, and threats (SWOT) analysis was also done to identify strategies for improvement. RESULTS: The number of neurosurgeons in each country is as follows: 370 in Indonesia, 10,014 in Japan, 152 in Malaysia, 134 in the Philippines, and 639 in Taiwan. With a large neurosurgical workforce, the high-income countries Japan and Taiwan have relatively high neurosurgeon to population ratios of 1 per 13,000 and 1 per 37,000, respectively. In contrast, the low- to middle-income countries Indonesia, Malaysia, and the Philippines have low neurosurgeon to population ratios of 1 per 731,000, 1 per 210,000, and 1 per 807,000, respectively. In terms of the number of training centers, Japan has 857, Taiwan 30, Indonesia 7, Malaysia 5, and the Philippines 10. In terms of the number of neurosurgical residents, Japan has 1000, Taiwan 170, Indonesia 199, Malaysia 53, and the Philippines 51. The average number of yearly additions to the neurosurgical workforce is as follows: Japan 180, Taiwan 27, Indonesia 10, Malaysia 4, and the Philippines 3. The different countries included in this report have many similarities and differences in their models and systems of neurosurgical education. Certain important strategies have been formulated in order for the system to be responsive to the needs of the catchment population: 1) establishment of a robust network of international collaboration for reciprocal certification, skills sharing, and subspecialty training; 2) incorporation of in-service residency and fellowship training within the framework of improving access to neurosurgical care; and 3) strengthening health systems, increasing funding, and developing related policies for infrastructure development. CONCLUSIONS: The varied situations of neurosurgical education in the East Asian region require strategies that take into account the different contexts in which programs are structured. Improving the education of current and future neurosurgeons becomes an important consideration in addressing the health inequalities in terms of access and quality of care afflicting the growing population in this region of the world.

Introduction: Spinal cord injury (SCI) is a significant and transforming event, with an estimated annual incidence of 40 cases per million individuals in North America. Considering the significance of accurate diagnosis and effective therapy in managing SCI, Machine Learning (ML) and Robot-Assisted Gait Training (RAGT) technologies hold promise for enhancing optimal practices and elevating the quality of care. This study aims to determine the impact of the ML and RAGT techniques employed on the outcome results of SCI. Methods: We reviewed four databases, including PubMed, Scopus, ScienceDirect, and the Cochrane Central Register of Controlled Trials (CENTRAL), until 20 August 2023. The keywords used in this study encompassed the following: a comprehensive search was executed on research exclusively published in the English language: machine learning, robotics, and spinal cord injury. Results: A comprehensive search was conducted across four databases, identifying 2367 articles following rigorous data filtering. The results of the odd ratio (OR) and confidence interval (CI) of 95% for the ASIA Impairment Scale, or AIS grade A, were 0.093 (0.011–0.754, p = 0.026), for AIS grade B, 0.875 (0.395–1.939, p = 0.743), for AIS grade C, 3.626 (1.556–8.449, p = 0.003), and for AIS grade D, 8.496 (1.394–51.768, p = 0.020). The robotic group exhibited a notable reduction in AS (95% CI = −0.239 to −0.045, p = 0.004) and MAS (95% CI = −3.657 to −1.066, p ≤ 0.001) measures. This study also investigated spasticity and walking ability, which are significant. Conclusions: The ML approach exhibited enhanced precision in forecasting AIS result scores. Implementing RAGT has been shown to impact spasticity reduction and improve walking ability.

COVID-19, a global pandemic, affects neurosurgical care in Indonesia. This study has objective to propose guideline and algorithm recommendation for the management of TBI patients during this pandemic, which can be used flexibly at neurosurgery centers, both in Indonesia and throughout the world. We performed retrospective Cohort analysis from TBI database at tertiary public general hospitals. All neurotrauma cases from mid-February until mid-August 2020 was included in this study. The chronology of COVID-19 pandemics impact in Indonesia was defined by early period from mid-February until end of May 2020, and late period are latter. All subjects undergone the screening and perioperative measures that based on our proposes scoring system and algorithm as follows. There are many guidelines that explain screening methods in neurosurgery patients in general, as well as neurotrauma in particular. But here, we proposed our own scoring and screening algorithm that has been developed based on conditions in Indonesia. In total of 757 neurotrauma cases data were collected from the pandemic starts in Indonesia. Screening is a crucial initial step in this pandemic period, not only for COVID patients, but also all patients who enter the emergency room. The use of PPE is a necessity in several neurosurgery centers, especially with high COVID-19 case rates. The management of neurotrauma patients with suspected and confirmed COVID-19 requires special attention, starting from admission of the patient in ER. Rapid scoring and screening are important and the highest level of PPE is mandatory during patient care.

Background: Glioblastoma, the predominant primary tumor among all central nervous systems, accounts for around 80% of cases. Prognosis in neuro-oncology involves assessing the disease’s progression in different individuals, considering the time between the initial pathological diagnosis and the time until the disease worsens. A noninvasive therapeutic approach called radiomic features (RFs), which involves the application of artificial intelligence in MRI, has been developed to address this issue. This study aims to systematically gather evidence and evaluate the prognosis significance of radiomics in glioblastoma using RFs. Methods: We conducted an extensive search across the PubMed, ScienceDirect, EMBASE, Web of Science, and Cochrane databases to identify relevant original studies examining the use of RFs to evaluate the prognosis of patients with glioblastoma. This thorough search was completed on 25 July 2024. Our search terms included glioblastoma, MRI, magnetic resonance imaging, radiomics, and survival or prognosis. We included only English-language studies involving human subjects, excluding case reports, case series, and review studies. The studies were classified into two quality categories: those rated 4–6 were considered moderate-, whereas those rated 7–9 were high-quality using the Newcastle–Ottawa Scale (NOS). Hazard ratios (HRs) and their 95% confidence intervals (CIs) for OS and PFS were combined using random effects models. Results: In total, 253 studies were found in the initial search across the five databases. After screening the articles, 40 were excluded due to not meeting the eligibility criteria, and we included only 14 studies. All twelve OS and eight PFS trials were considered, involving 1.639 and 747 patients, respectively. The random effects model was used to calculate the pooled HRs for OS and PFS. The HR for OS was 3.59 (95% confidence interval [CI], 1.80–7.17), while the HR for PFS was 4.20 (95% CI, 1.02–17.32). Conclusions: An RF-AI-based approach offers prognostic significance for OS and PFS in patients with glioblastoma.

Background: Intracranial atherosclerotic stenosis (ICAS) is a major global cause of stroke. Patient with high risk of recurring strokes despite standard medication therapy usually needs aggressive medical treatment such as self-expanding and balloon-expandable stents. Drug-eluting stents (DESs), with anti-vascular endothelial cell proliferation drugs, aim to inhibit smooth muscle cells proliferation and migration, providing a promising alternative to standard bare metal stents (BMS). This study compared the advantages and safety of DES to BMS in symptomatic ICAS patients. Methods: A comprehensive search was conducted through PubMed, Cochrane Central Register of Controlled Trials, and Science Direct in May 2024, adhering to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Randomized trials comparing DES and BMS for symptomatic intracranial and vertebral artery stenosis were included in the study. Meta-analysis was carried out by RevMan software to analyze and odds ratio (OR) for primary and secondary outcomes. Results: Three randomized controlled trials (n = 491) were included in the study. DES significantly reduced instent restenosis (ISR) compared to BMS (OR 0.26, 95% confidence interval [CI]: 0.16–0.45, P < 0.00001) and lowered the risk of ischemic stroke in the target vessel (OR 0.20, 95% CI: 0.06–0.65, P = 0.008). No significant difference was observed in all-cause mortality (OR 0.80, 95% CI: 0.21–3.02, P = 0.74) or technical success rates. Symptomatic ISR within 1 year was markedly lower with DES (OR 0.09, 95% CI: 0.01–0.73, P = 0.02). Conclusion: Intravascular DES is more effective than BMS in lowering risk of ISR and incidence of ischemic stroke in symptomatic ICAS.