Latest AI and machine learning research in neurology for healthcare professionals.
INTRODUCTION: Machine learning methods have emerged as objective tools to evaluate operative perform...
Physiological signal processing plays a key role in next-generation human-machine interfaces as phys...
INTRODUCTION: The aging of the population and the high incidence of those over 80 lead to an inevita...
The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed...
Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investi...
Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging cl...
In recent years, artificial intelligence (AI) technology has promoted the development of electroence...
The current practices of designing neural networks rely heavily on subjective judgment and heuristic...
Disorders of autonomic functions are typically characterized by disturbances in multiple organ syste...
OBJECTIVES: To conduct an external validation of an automated artificial intelligence (AI) diagnosti...
The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroenceph...
Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of...
Neuromorphic computing inspired by the human brain is highly desirable in the artificial intelligenc...
BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion...
PURPOSE: Spinal augmentation procedures (SAP) are standard procedures for vertebral compression frac...
Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enab...
BACKGROUND: The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA) was developed...
During surgery for foci-related epilepsy, neurosurgeons face significant difficulties in identifying...
LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is inspired from...
Machine learning (ML) could have advantages over traditional statistical models in identifying risk ...
OBJECTIVE: Magnetic resonance imaging (MRI) is commonly used to evaluate cervical spinal canal steno...