Latest AI and machine learning research in neurology for healthcare professionals.
BACKGROUND: Video electroencephalographies (VEEGs) are often affected by artifacts, which can diminish clinicians' efficiency in interpreting VEEG data and potentially result in diagnostic errors. Conversely, certain ictal VEEG artifacts caused by automatisms, such as rhythmic chewing and blinking, may offer significant diagnostic clues for temporal lobe epilepsy. To address the challenges mention...
The past decade has witnessed notable advancements in clinical neuroimaging facilitated by technological innovations and significant scientific discoveries. In conjunction with Investigative Radiology 's 60th anniversary, this review examines key contributions from the past 10 years, emphasizing the journal's most accessed articles and their impact on clinical practice and research. Advances in im...
OBJECTIVE: Cerebrovascular autoregulation (CA) maintains adequate cerebral blood flow (CBF) during changes in cerebral perfusion pressure (CPP) and ca...
OBJECTIVE: This study aimed to develop a deep learning model capable of accurately forecasting non-apneic sleep arousals, which are brief awakenings t...
Model fusion aims to integrate several deep neural network (DNN) models' knowledge into one by fusing parameters, and it has promising applications, s...
Purpose To develop a self-supervised text-vision framework to detect abnormalities on brain MRI scans by leveraging free-text neuroradiology reports, ...
Machine learning (ML)-based methods have been proposed as a potential approach for identifying candidate drugs to be repurposed as disease-modifying t...
BACKGROUND: Central retinal artery occlusion (CRAO) is a vision-threatening neuro-ophthalmic emergency, analogous to acute ischemic stroke. Delayed pr...
OBJECTIVES: Artificial intelligence (AI) applications are being increasingly explored in pain medicine due to AI's ability to handle multidimensional ...
BACKGROUND: Radiotherapy planning traditionally requires a dedicated simulation CT (sCT), which can introduce delays in initiating treatment. This is ...
The present study investigated neurocognitive underpinnings of age categorization of faces by integrating behavioral and neuroimaging measures in youn...
BACKGROUND: Epilepsy surgery is an important intervention for treatment-resistant epilepsy, butthe ability to predict long-term seizure freedom post-s...
PurposeTo assess the effectiveness of robot-assisted training (RAT) plus acupuncture therapy (AT) on lower limb functional recovery in stroke patients...
Socioeconomic status (SES) has been linked to brain-based markers, but most studies rely on conventional statistical methods that overlook the complex...
Episodic memory integrates what, where, and when of experience into a coherent autobiographical narrative. Decades of research have identified hippoca...
Background: Upper extremity motor impairment is common after stroke. The Hybrid Assistive Limb single-joint type (HAL-SJ), an exoskeletal robot with b...
Digital therapeutics, enabled by advanced machine learning algorithms and medical wearable devices, offer a promising approach to streamline diagnosti...
OBJECTIVES: Advanced MRI is recommended for the clinical evaluation of patients with coma. However, the implementation of these guidelines has been hi...
OBJECTIVES: Assessing regional brain atrophy on 3D-T1w imaging is crucial for evaluating neurodegenerative disorders. However, high-quality volumetric...
OBJECTIVES: This study aimed to investigate the association of COX-2 gene polymorphisms with key clinical features of migraine, including headache ons...