Parkinson’s disease (PD) is a progressive neurodegenerative disorder marked by motor and non-motor dysfunctions that severely compromise patients’ quality of life. While pharmacological treatments provide symptomatic relief in the early stages, advanced PD often requires neurosurgical interventions, such as deep brain stimulation (DBS) and focused ultrasound (FUS), for effective symptom management. A significant challenge in optimizing these therapeutic strategies is the early identification and recruitment of suitable candidates for clinical trials. This review explores the role of artificial intelligence (AI) in advancing neurosurgical and neuroscience interventions for PD, highlighting the ways in which AI-driven platforms are transforming clinical trial design and patient selection. Machine learning (ML) algorithms and big data analytics enable precise patient stratification, risk assessment, and outcome prediction, accelerating the development of novel therapeutic approaches. These innovations improve trial efficiency, broaden treatment options, and enhance patient outcomes. However, integrating AI into clinical trial frameworks presents challenges such as data standardization, regulatory hurdles, and the need for extensive validation. Addressing these obstacles will require collaboration among neurosurgeons, neuroscientists, AI specialists, and regulatory bodies to establish ethical and effective guidelines for AI-driven technologies in PD neurosurgical research. This paper emphasizes the transformative potential of AI and technological innovation in shaping the future of PD neurosurgery, ultimately enhancing therapeutic efficacy and patient care.

Background: 5-Aminolevulinic acid (5-ALA) serves as a precursor in the heme biosynthesis pathway, resulting in the selective accumulation of protoporphyrin IX (PpIX) within glioma cells. This property facilitates fluorescence-guided resection (FGR) in high-grade gliomas (HGGs), enhancing surgical precision and oncological results. Nonetheless, its clinical implementation is restricted by factors such as accessibility, cost, and technical limitations. Methods: A systematic review of PubMed literature (2019–2024) was conducted to assess the efficacy of 5-ALA in HGG surgery compared to conventional white light microscopy. Studies focusing on non-neurosurgical applications, pediatric populations, and non-HGG indications were excluded. Results: Nineteen articles met the criteria. Recent studies indicate that 5-ALA-guided resection significantly enhances gross total resection (GTR) rates compared to white light surgery (75.4% vs. 54.3%, p < 0.001). Patients receiving 5-ALA-assisted resection exhibit enhanced progression-free survival (PFS) at 6 months (median 8.1 months compared to 5.4 months, p = 0.002) and overall survival (OS) (median 15.2 months versus 12.3 months, p = 0.008). The necessity for specialized neurosurgical microscopes equipped with blue light filters restricts accessibility, especially in low-resource environments. Recent advancements in fluorescence-enhancing technologies, particularly loupe-based systems, have demonstrated increases in fluorescence intensity by up to tenfold through direct emission. Sodium fluorescein, originally designed for ophthalmological use, has been adapted for enhancing contrast in intracranial tumors; however, its non-specific binding to serum albumin restricts its accuracy in glioma resection. Conclusions: Recent publications demonstrate that 5-ALA fluorescence-guided surgery significantly improves gross total resection rates and survival outcomes in patients with high-grade gliomas. Although it offers clinical advantages, cost and equipment constraints continue to pose substantial obstacles to broad implementation. Additional research is required to enhance fluorescence-guided techniques and increase accessibility in resource-constrained environments.

Background: Disparities in neuro-oncological care between high-income and low- and middle-income countries (LMICs) are well documented, yet region-specific data from Latin America remain limited. This review evaluates epidemiologic trends, access to care, and systemic challenges in brain tumor management across Latin American LMICs, using Argentina as a case study. Methods: A systematic review of peer-reviewed literature was conducted focusing on brain tumor incidence, mortality, risk factors, and availability of diagnostics and treatments in Latin America. Socioeconomic, cultural, and systemic barriers were also analyzed. Results: Latin America exhibits some of the highest global brain tumor mortality rates, with Brazil reporting age-standardized rates exceeding 4.5 per 100,000. Glioblastomas are frequently diagnosed at younger ages, often in the fifth decade of life, compared to the global average. Meningioma incidence has increased by 15–20% over the last decade, yet region-wide data remain fragmented. Access to neuroimaging, neurosurgery, radiotherapy, and chemotherapy is limited, with up to 60% of patients relying solely on under-resourced public health systems. Less than 30% of hospitals in rural areas have MRI availability, and continuous professional training is infrequent. Innovative adaptations, such as awake craniotomy, are used in some LMIC centers in response to equipment scarcity. Conclusions: Brain tumor care in Latin America is hindered by limited epidemiological data, restricted access to diagnostics and treatment, and insufficient workforce training. Targeted investments in healthcare infrastructure, international educational collaborations, and policy-level reforms are critical to reducing disparities and improving outcomes in neuro-oncology across the region.

Background: Primary intracranial sarcomas (PISs) are very rare malignant tumors, and there is paucity of data on it, exclusively in patients <18 years old. We report pediatric PIS at a tertiary hospital in Peru, where the incidence of PIS has increased in recent years. Methods: We retrospectively analyzed data in children diagnosed with PIS based on clinical presentation, imaging studies, and histopathology between January 2020 and December 2023. Results: Twenty-five cases were identified. The median age was 5 years. There is slight female predominance (56%). On presentation, 68% of patients had features of intracranial hypertension (ICH), others had convulsions or motor deficits. There was radiologic evidence of cerebral hemorrhage in 80% of those with features of ICH and convulsion. All but one case had a supratentorial tumor. Emergency craniotomy was done in 84% of cases, and gross total resection (GTR) was achieved in the first surgery in 72% of cases. We used an adjuvant chemotherapyradiotherapy-chemotherapy (CTX-RT-CTX) regimen in 72% of cases, but 12% started this scheme 2 weeks after surgical resection. The cases followed up for more than a year that were managed with CTX-RT-CTX after GTR had a survival greater than a year, compared to the cases that received complementary treatment after 4 weeks. Conclusion: PIS among children represents an infrequent pathology that, in the last years, its incidence has increased in Peru. The presence of intracerebral hemorrhage is a very suggestive finding of this diagnosis; therefore, emergent surgical management is an option before an irreversible ICH presents. Adjuvant treatment with the CTX-RT-CTX regimen started 2 weeks after GTR may improve survival in children with PIS.

The COVID-19 pandemic in 2020 profoundly impacted healthcare worldwide, and Peru was particularly affected, experiencing the highest COVID-19 case fatality rate globally. We conducted a retrospective comparative study of patients presenting with aneurysmal subarachnoid hemorrhage (SAH) at a public Peruvian national referral center specializing in cerebrovascular diseases. Two study periods were considered, one during the first wave of the COVID-19 pandemic and a second identical period in 2019. Variables included patient demographics, comorbidities, COVID-19 infection status, clinical presentation, treatment approaches, and hospital outcomes. We analyzed 114 patients with aneurysmal SAH, 65 (57.0%) treated in 2019 and 49 (43.0%) in 2020. The mean time from emergency admission to the day of definitive treatment during 2019 and 2020 was 2.72 and 1.93 days, respectively. Likewise, the mean time from symptom onset to the date of definitive treatment was 6.71 and 7.70 days, respectively. We identified significant associations between complications such as sepsis, respiratory failure, acute kidney failure, and hospital mortality. The proportion of fatalities was significantly higher in 2020 compared to 2019 (36.7% vs. 15.4%, respectively). Interestingly, there was no statistically significant association between COVID-19 infection status and mortality during the 2020 period. Patients with aneurysmal SAH treated during the pandemic had a higher proportion of sepsis, respiratory failure, acute kidney failure, and mortality compared to the pre-pandemic period. Reallocating healthcare resources to prioritize COVID-19 patients may have inadvertently neglected or delayed care for patients with other emergency conditions, such as aneurysmal SAH. This highlights the importance of maintaining adequate care for non-infectious emergencies during a pandemic.

Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disorder that significantly impacts patients’ quality of life due to both motor and non-motor symptoms. While pharmacological interventions can alleviate symptoms in the early stages, advanced cases often become refractory to medication, requiring additional therapeutic approaches. Neurosurgical interventions such as deep brain stimulation (DBS) and focused ultrasound (FUS) have emerged as effective treatments for patients with medication-resistant motor symptoms. However, one of the biggest challenges in advancing these therapies is the early identification and recruitment of suitable candidates for clinical trials, essential for refining and expanding these treatments. This review explores the integration of artificial intelligence (AI) and other technological innovations in enhancing clinical trial processes for neurosurgical treatments in PD. AI platforms are revolutionizing patient selection for trials by providing advanced tools for patient stratification, risk assessment, and trial design optimization. These technologies improve trial efficiency and facilitate more personalized treatment strategies that enhance surgical outcomes and minimize adverse effects. Despite the potential of AI to transform neurosurgical care, challenges remain, particularly concerning the standardization of AI applications and their seamless integration into clinical practice. Multidisciplinary collaboration will be crucial to overcoming these obstacles and establishing evidence-based guidelines for the safe and effective use of AI in neurosurgical trials. Through continued innovation and interdisciplinary efforts, AI has the potential to revolutionize patient care and research in PD, leading to significant improvements in neurosurgical interventions and patient outcomes.