Latest AI and machine learning research in neurology for healthcare professionals.
Open-source AI models are increasingly applied in medical imaging, yet their effectiveness in detecting and classifying spinal stabilization systems remains underexplored. This study compares ChatGPT-4o (a large language model) and BiomedCLIP (a multimodal model) in their analysis of posturographic X-ray images (AP projection) to assess their accuracy in identifying the presence, type (growing vs...
: This study aimed to develop and validate comorbidity-based severity adjustment methods for acute cerebrovascular disease by recalibrating the Charlson Comorbidity Index (CCI) and constructing a CCS-based comorbidity index to improve mortality risk prediction. : Using the Korea Disease Control Agency's Discharge Injury In-depth Survey Dataset (2013-2022), we applied Cox proportional hazards regre...
BACKGROUND: Electroencephalography (EEG) is a noninvasive, cost-effective, and robust tool, which directly measures in vivo neuronal mass activity wit...
Introduction: Operative management of spinal metastatic disease is largely for symptom palliation and revolves around the expectation that postoperati...
An accurate and reliable functional prognosis is vital to stroke patients addressing rehabilitation, to their families, and healthcare providers. This...
Speech dysarthria is a key symptom of neurological conditions like ALS, yet existing AI models designed to analyze it from audio signal rely on handcr...
Developing effective treatments for Alzheimer's disease (AD) likely requires a deep understanding of molecular mechanisms. Integration of transcriptom...
BACKGROUND CONTEXT: Survival prediction models for patients with spinal metastases are crucial for guiding clinical decision-making and optimizing tre...
While foundation models excel in text, image, and video domains, the critical biological signals, particularly electroencephalography(EEG), remain u...
Our study presents PNN-UNet as a method for constructing deep neural networks that replicate the planarian neural network (PNN) structure in the con...
A three-dimensional convolutional neural network was developed to classify T1-weighted brain MRI scans as healthy or Alzheimer. The network comprise...
Parkinson's disease (PD) is a complex neurodegenerative disorder lacking effective disease-modifying treatments. In this study, we integrated large-sc...
BACKGROUND: Hippocampal atrophy is a key marker of Alzheimer's disease (AD) and mild cognitive impairment (MCI). Diverse artificial intelligence (AI) ...
Dravet syndrome is a developmental and epileptic encephalopathy, characterized by the early onset of drug-resistant seizures and various comorbiditi...
Harmonization of T1-weighted MR images across different scanners is crucial for ensuring consistency in neuroimaging studies. This study introduces ...
Cognitive fatigue is a psychological condition characterized by opinions of fatigue and weakened cognitive functioning owing to constant stress. Cogni...
Cerebral aneurysms are pathological dilations of intracranial arteries that can rupture with devastating consequences, including subarachnoid hemorr...
Parkinson's disease (PD) is a neurodegenerative disorder, manifesting with motor and non-motor symptoms. Depressive symptoms are prevalent in PD, af...
Stroke is a major public health problem, affecting millions worldwide. Deep learning has recently demonstrated promise for enhancing the diagnosis a...
Partial hand amputations significantly affect the physical and psychosocial well-being of individuals, yet intuitive control of externally powered p...