AI Medical Compendium Journal:
Clinical neurology and neurosurgery

Showing 1 to 10 of 30 articles

Development and validation of a predictive machine learning model for postoperative long-term diabetes insipidus following transsphenoidal surgery for sellar lesions.

Clinical neurology and neurosurgery
OBJECTIVE: Diabetes Insipidus (DI) is a common complication that occurs following transsphenoidal surgery for sellar lesions. DI is usually transient but can be permanent in select patients. Prior studies have described preoperative risk factors for ...

Artificial intelligence versus neurologists: A comparative study on multiple sclerosis expertise.

Clinical neurology and neurosurgery
INTRODUCTION: Multiple sclerosis (MS) is an autoimmune neurodegenerative disease affecting the central nervous system. MS diagnosis is complex, requiring magnetic resonance imaging and cerebrospinal fluid analysis due to the lack of definitive biomar...

Optimizing stroke prediction using gated recurrent unit and feature selection in Sub-Saharan Africa.

Clinical neurology and neurosurgery
BACKGROUND: Stroke remains a leading cause of death and disability worldwide, with African populations bearing a disproportionately high burden due to limited healthcare infrastructure. Early prediction and intervention are critical to reducing strok...

Can we rely on machine learning algorithms as a trustworthy predictor for recurrence in high-grade glioma? A systematic review and meta-analysis.

Clinical neurology and neurosurgery
Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence from treatment-related changes, such as radionecrosis, is a major diagnostic challenge. Machine lear...

Research on predicting radiographic exposure time in imaging based on neural network prediction models.

Clinical neurology and neurosurgery
OBJECTIVE: To explore the anatomical and clinical factors that affect the radiographic exposure time in radial artery cerebral angiography and to establish a model.

Integrating AI-driven wearable devices and biometric data into stroke risk assessment: A review of opportunities and challenges.

Clinical neurology and neurosurgery
Stroke is a leading cause of morbidity and mortality worldwide, and early detection of risk factors is critical for prevention and improved outcomes. Traditional stroke risk assessments, relying on sporadic clinical visits, fail to capture dynamic ch...

Machine learning for predicting poor outcomes in aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis involving 8445 participants.

Clinical neurology and neurosurgery
Early prediction of poor outcomes in patients impacted with aneurysmal subarachnoid hemorrhage (aSAH) is crucial for timely intervention and effective management. This systematic review and meta-analysis aimed to evaluate the performance of machine l...

Artificial intelligence versus clinical judgement: how accurately do generative models reflect CNS guidelines for chiari malformation?

Clinical neurology and neurosurgery
OBJECTIVE: This study investigated the response and readability of generative artificial intelligence (AI) models to questions and recommendations proposed by the 2023 Congress of Neurological Surgeons (CNS) guidelines for Chiari 1 malformation.

Intraoperative label-free tissue diagnostics using a stimulated Raman histology imaging system with artificial intelligence: An initial experience.

Clinical neurology and neurosurgery
BACKGROUND: Accurate intraoperative tissue diagnostics could impact on decision making regarding the extent of resection (EOR) during brain tumor surgery. Stimulated Raman histology (SRH) is a label-free optical imaging method that uses different bio...