Traditional neurosurgical training involves a steep learning curve. The introduction of advanced simulation technologies, like virtual reality, provides an alternative method for skill acquisition, allowing for repeated practice and objective assessment. This study focuses on evaluating the learning curve associated with lumbar pedicle Kirschner wire insertion using a virtual fluoroscopic simulator among neurosurgical residents.

Dysplastic cerebellar gangliocytoma (DCG) is a rare cerebellar tumor glioneuronal and neuronal tumor with phosphatase and tensin homolog (PTEN) identified as a key altered gene. The aim of this study is to establish DCG diagnostic and outcome trends over a six-decade and present cases from our institution.

Degenerative cervical myelopathy (DCM) is a debilitating condition caused by compression of the spinal cord. Despite established surgical treatments, accurate diagnosis and prognostication remain challenging in part due to the variability in clinical presentation and lack of screening tools. Machine learning (ML) has emerged as a promising approach to address these challenges through its predictive capabilities for diagnosis, decision-making, and prognostication. Given the recent advent of ML, there is a need to systematically synthesize its applications to the treatment of patients with DCM.

While some patients require immediate surgery for acute subdural hematoma (ASDH), others can be managed conservatively. A subset of patients, however, may experience the progression of ASDH to a relevant chronic subdural hematoma (CSDH). This systematic review aims to synthesize studies focusing on ASDH which progress to CSDH.

Isolated spinal artery aneurysms (ISAAs) are rare, often presenting with sub-arachnoid hemorrhage (SAH) and severe neurological deficits. Conclusive evidence about the best management approach is lacking.

The technique for extracranial-intracranial (EC-IC) bypass was introduced in 1976. Over the subsequent 45 years, indications and surgical techniques have significantly evolved. This study aims to analyze the trends in patient demographics, bypass techniques, and clinical indications for bypass surgeries performed at our institution from 1976 to 2020.

The Calabria Decree (Law No. 145 of 2018) and its subsequent amendments introduced reforms aimed at improving the employment and training conditions of medical residents in Italy. Notably, the decree allowed for the early hiring of residents, starting from their second year of specialization, with permanent contracts upon completion of their training. This study explores the impact of these reforms on neurosurgery residents, specifically examining the effects on their professional development, education, and well-being.

While intraoperative transcranial motor evoked potential (tcMEP) monitoring is widely used in spinal and brain surgery, it is sometimes not used because it can exhibit low specificity and give false-positive results.

Symptomatic lumbar degenerative changes impact millions of patients per year. Recent technological advances have increased the usability of robot-assisted spinal fusions to treat this pathology. Although the safety profile of robotic systems appears favorable, the impact of robotics on surgical outcomes and efficiency remains unclear.

Repetitive transcranial magnetic stimulation (rTMS) of the dorsolateral prefrontal cortex (DLPFC) may improve motor and non-motor symptoms in Parkinson's disease (PD). This meta-analysis assessed the efficacy of 10 unilateral rTMS sessions in PD.

Managing foramen magnum meningiomas (FMMs) poses challenges due to their anatomical complexity. Surgical resection aimed at achieving gross total resection (GTR) is the primary therapeutic option; however, the proximity to nerves and arteries complicates resection and is associated with significant morbidity and mortality rates, reported to reach as high as 50% and 25%, respectively. Stereotactic radiosurgery (SRS) is a non-invasive radiotherapeutic modality that has been extensively used to treat intracranial meningiomas and is associated with high rates of favorable outcomes and low complications. This study aimed to evaluate the efficacy and safety of SRS for FMMs.

This narrative review presents deep learning-based strategies for generating synthetic 3D CT-like images from biplanar or multiplanar 2D X-ray data. Current limitations of conventional CT imaging are discussed, hence emphasizing the potential of synthetic CT reconstruction as an alternative technique in certain scenarios. Previous non deep learning approaches for 3D reconstruction from 2D X-rays are presented, indicating their weaknesses and thus pointing out the potential benefits of deep learning techniques. Convolutional neural networks (CNNs), generative adversarial networks (GANs), and conditional diffusion processing (CDP) are introduced, as they demonstrate great potential for synthetic CT generation in multiple studies over the last few years. The review further presents the potential clinical applications, existing challenges and latest research advancements of deep learning strategies for 3D reconstruction from 2D X-rays.

Glioblastoma is the most aggressive primary brain tumor, with poor prognosis following initial identification. Current diagnostic methods, including neuroimaging and molecular pathology, face several limitations in tumor delineation, differentiation of progression from treatment effects, and classification of tumor grade. Artificial intelligence (AI) and machine learning (ML) have been increasingly investigated for its potential in addressing such challenges. This narrative review examines existing AI applications in glioblastoma screening and classification, as well as their associated methodological shortcomings. A comprehensive literature search was conducted in MEDLINE for studies published in the past five years applying ML methods to glioblastoma screening and classification. Studies which were not peer reviewed, did not discuss screening or classification, or lacked a clearly defined ML methodology were excluded. Study designs, training dataset type, and model efficacies were reviewed for narrative evidence synthesis. AI-based omics models frequently applied genomic, transcriptomic, methylation status, and Raman spectroscopy data to glioblastoma classification. Non-omics AI applications frequently involved imaging-based methods, in addition to histopathologic and clinical studies. Accuracies exceeding 90% were observed in several studies for the identification and classification of glioblastoma. Despite this, challenges remain in clinical implementation due to dataset heterogeneity, inconsistent model validation, and lack of standardized reporting methodology. While large language models are an emerging area of interest, few studies investigated their uses in the screening or classification of GBM. AI offers the potential for significant advancements in GBM screening and classification, but widespread clinical adoption requires improved application of existing reporting guidelines. Future research should focus on model interpretability, further development of high-quality datasets, and implementation.

The Italian Association of Neuro-Oncology (AINO) and the Italian Society of Neurosurgery (SINch) promoted a national survey to explore how the 2021 WHO molecular diagnostic criteria for gliomas have been implemented into clinical practice.

Recent advancements in artificial intelligence, particularly in large language models (LLMs), have catalyzed new opportunities within medical domains, including neurosurgery. This study aims to evaluate and compare the performance of two advanced LLMs - ChatGPT-Omni and Gemini -in addressing clinical case inquiries on various neurosurgical conditions.

Proximal junctional kyphosis (PJK) is a common complication following adult spinal deformity (ASD) surgery and puts patients at an increased risk for neurological injury. As reoperation continues to be the mainstay treatment, there is utility in identifying independent preoperative risk factors for PJK development. The aim of this study was to determine whether a history of anterior cervical discectomy and fusion (ACDF) predicts increased incidence of PJK after ASD correction.

The aim of this pilot registry study was to investigate the use of Pycnogenol (French maritime pine bark, standardized extract) in subjects 2 weeks after an episode of sudden loss of vision (SLV).

European regulations on medical devices have been introduced to improve medical standards. However, these changes are leading to the transient lack of medical tools with possible disadvantages. Herein, the problem is addressed for the first time in neurosurgery with regards to ventriculo-atrial shunt (VAS).