AI Medical Compendium Journal:
Clinical neurology and neurosurgery

Showing 11 to 20 of 30 articles

Deciphering seizure semiology in corpus callosum injuries: A comprehensive systematic review with machine learning insights.

Clinical neurology and neurosurgery
INTRODUCTION: Seizure disorders have often been found to be associated with corpus callosum injuries, but in most cases, they remain undiagnosed. Understanding the clinical, electrographic, and neuroradiological alternations can be crucial in delinea...

Machine learning model for predicting stroke recurrence in adult stroke patients with moyamoya disease and factors of stroke recurrence.

Clinical neurology and neurosurgery
OBJECT: The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence.

Chat-GPT on brain tumors: An examination of Artificial Intelligence/Machine Learning's ability to provide diagnoses and treatment plans for example neuro-oncology cases.

Clinical neurology and neurosurgery
OBJECTIVE: Assess the capabilities of ChatGPT-3.5 and 4 to provide accurate diagnoses, treatment options, and treatment plans for brain tumors in example neuro-oncology cases.

Deep learning in neuroimaging of epilepsy.

Clinical neurology and neurosurgery
In recent years, artificial intelligence, particularly deep learning (DL), has demonstrated utility in diverse areas of medicine. DL uses neural networks to automatically learn features from the raw data while this is not possible with conventional m...

Machine learning for outcome prediction of neurosurgical aneurysm treatment: Current methods and future directions.

Clinical neurology and neurosurgery
INTRODUCTION: Machine learning algorithms have received increased attention in neurosurgical literature for improved accuracy over traditional predictive methods. In this review, the authors sought to assess current applications of machine learning f...

Deep Learning model-based approach for preoperative prediction of Ki67 labeling index status in a noninvasive way using magnetic resonance images: A single-center study.

Clinical neurology and neurosurgery
OBJECTIVES: Ki67 is an important biomarker of pituitary adenoma (PA) aggressiveness. In this study, PA invasion of surrounding structures is investigated and deep learning (DL) models are established for preoperative prediction of Ki67 labeling index...

Can we predict anti-seizure medication response in focal epilepsy using machine learning?

Clinical neurology and neurosurgery
OBJECTIVE: The aim of this study was to evaluate the feasibility of machine learning approach based on clinical factors and diffusion tensor imaging (DTI) to predict anti-seizure medication (ASM) response in focal epilepsy. We hypothesized that ASM r...

Machine learning models of ischemia/hemorrhage in moyamoya disease and analysis of its risk factors.

Clinical neurology and neurosurgery
OBJECT: This study aimed to determine the risk factors of ischemic/hemorrhagic stroke in patients suffering moyamoya disease (MMD), as well as to compare the effects of six analysis methods.

Resection of the medial wall of the cavernous sinus in functioning pituitary adenomas: Technical note and outcomes in a matched-cohort study.

Clinical neurology and neurosurgery
BACKGROUND: Parasellar dural invasion can be associated with treatment failure after excision of functioning pituitary adenomas. Because the medial wall of the cavernous sinus is a common site of microscopic disease, we hypothesize that its resection...