Neurosurgery

Artificial intelligence in diagnostic neurosurgery: promising results and need for rigorous validation.

Commercial AI platforms improve reproducibility but fail to meet clinical precision thresholds for intracranial aneurysm measurement: implications for serial surveillance.

BACKGROUND: Intracranial aneurysm affect 3-7% of the global population, with rupture causing > 80% of non-traumatic subarachnoid hemorrhage and approximately 50% mortality. Clinical management relies on precise measurement of aneurysm neck width and maximum length, where ≥ 1 mm growth signals elevated rupture risk. computed tomography angiography enables non-invasive monitoring but manual measurem...

Development and validation of interpretable machine learning models for predicting long-term functional outcomes in elderly patients with aneurysmal subarachnoid hemorrhage.

BACKGROUND: Accurate prognostication in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH) remains challenging due to high morbidity and ...

Comparative diagnostic capability of large language models in neurosurgery.

OBJECTIVE: OpenAI, Google, and Microsoft have recently developed popular large language models (LLMs) with incredible clinical applications. LLMs spec...

Physics constrained graph neural network for real time prediction of intracranial aneurysm hemodynamics.

Intracranial aneurysms (IAs) are life-threatening vascular conditions requiring accurate risk assessment to guide treatment. Hemodynamic biomarkers su...

Leveraging in-silico deep learning and computational analyses to predict the pathogenicity of ROBO4 variants of uncertain significance in aortic aneurysm and dissection patients.

Variants of uncertain significance (VUS) in genes implicated in thoracic aortic aneurysm (TAA) present clinical challenges due to ambiguous pathogenic...

Clinical Profile of Neurosurgical Consultations from the Emergency Department : A Retrospective Analysis.

OBJECTIVE: This study aimed to retrospectively analyze consultations requested from the emergency departments (EDs) to the neurosurgery (NS) departmen...

Risk Factors for Postoperative Delirium in Nonintensive Care Unit Patients: Machine Learning Approach.

Postoperative delirium is a frequent and serious complication lacking effective prediction tools for general ward patients. This study aimed to identi...

Diagnostic Accuracy of Cerebrospinal Fluid Presepsin vs. Procalcitonin in Post-Neurosurgical Bacterial Ventriculitis/Meningitis: A Machine Learning Analytical Approach.

This study aimed to evaluate the diagnostic accuracy of cerebrospinal fluid presepsin and procalcitonin in patients who had undergone neurosurgery bet...

Cardiovascular measures from abdominal MRI provide insights into abdominal vessel genetic architecture.

BACKGROUND: Cardiovascular disease remains a major source of morbidity and mortality, and population imaging studies have yielded insights into diseas...

Trends and Scientific Evolution in Endovascular Aneurysm Repair (EVAR) Research: A Comprehensive Bibliometric Study Using VOSviewer.

BACKGROUND: Endovascular aneurysm repair (EVAR) has become a standard treatment for abdominal aortic aneurysms. However, the evolution of EVAR researc...

Performance of Machine Learning Models in Predicting Outcomes After ACDF: A Systematic Review and Meta-Analysis of 443 000 Patients.

Study DesignSystematic review and meta-analysis.ObjectiveDirect head-to-head comparison of machine learning models aiming to predict outcomes in Anter...

Elucidate senescence-related gene signature and immune infiltration landscape in abdominal aortic aneurysm.

BACKGROUND: Abdominal aortic aneurysm (AAA) refers to a lasting enlargement of the abdominal aorta. Senescence, a major risk factor of AAA, demonstrat...

Multimodal imaging of acquired aortic diseases: clinical efficacy, comparative analysis, and future perspectives.

Aortic diseases, particularly acute aortic syndromes (AAS) and aortic aneurysms (AA), represent critical cardiovascular conditions with high mortality...

TomoRay cranial: synthesis of cranial CT imaging from biplanar radiographs using a generative adversarial network.

OBJECTIVES: Besides clinical examination, cranial CT plays a critical role in diagnostics in neurosurgery. In trauma cases or perioperatively, having ...

Machine Learning Predictive Models for Prognosis in Patients undergoing Endovascular Abdominal Aortic Aneurysm Repair.

BACKGROUND: An increasing number of patients with abdominal aortic aneurysms (AAAs) are opting for endovascular aneurysm repair (EVAR), and predicting...

Predicting Cerebral Aneurysm Recurrence after Coil Embolization: A Novel Deep Learning Approach Using Time-of-flight Magnetic Resonance Angiography.

The recurrence of cerebral aneurysms after coil embolization remains a significant concern in clinical practice. This study introduced a novel approac...

Predictors of Outcome Clusters in Patients With Unruptured Intracranial Aneurysms Treated With Microsurgery: An Unsupervised Machine Learning Analysis.

BACKGROUND AND OBJECTIVES: Identifying surgical candidates who are prone to poor outcomes is crucial for adapting treatment and ensuring optimal outco...

Use of artificial intelligence-assisted measurement of aortic diameter in clinical decision making about abdominal aortic aneurysm repair.

OBJECTIVES: Decisions to perform abdominal aortic aneurysm (AAA) repair are dependent on aneurysm size, but variation in size measurement leads to inc...

The use of Artificial Intelligence in neurosurgical manuscript writing: Journal specific policies and their implementation.

INTRODUCTION: The use of Artificial Intelligence (AI) has grown dramatically in recent years. In addition to its use for data analysis, its applicatio...