Latest AI and machine learning research in neurology for healthcare professionals.
BACKGROUND: Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high accuracy and ability to process large volumes of data. It has been used to predict acute stroke recovery; however, whether machine learning would be effective for predicting rehabil...
BACKGROUND: After stroke, kinematic measures obtained with non-robotic and robotic devices are highly recommended to precisely quantify the sensorimotor impairments of the upper-extremity and select the most relevant therapeutic strategies. Although the ArmeoSpring exoskeleton has demonstrated its effectiveness in stroke motor rehabilitation, its interest as an assessment tool has not been suffici...
In the field of brain-computer interfaces, it is very common to use EEG signals for disease diagnosis. In this study, a style regularized least square...
OBJECTIVES: Sleep disturbances are common in people with dementia and increase with the severity of the disease. Sleep disturbances are complex and ca...
BACKGROUND AND PURPOSE: Accurate prediction using simple and changeable variables is clinically meaningful because some known-predictors, such as stro...
BACKGROUND: Robot-assisted pedicle screw insertion has gained popularity in the spinal surgery field. Due to high cost, these spinal robots are not ex...
Arterial brain vessel assessment is crucial for the diagnostic process in patients with cerebrovascular disease. Non-invasive neuroimaging techniques...
OBJECTIVE: To investigate the effect of a social robot intervention on sleep and motor activity in nursing home residents living with dementia and chr...
Both normal ageing and neurodegenerative diseases cause morphological changes to the brain. Age-related brain changes are subtle, nonlinear, and spati...
PURPOSE: The robot-assisted approach for Ivor Lewis esophagectomy offers an enlarged, three-dimensional overview of the intraoperative situs. The vaga...
BACKGROUND: Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput,...
Current hardware approaches to biomimetic or neuromorphic artificial intelligence rely on elaborate transistor circuits to simulate biological functio...
Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative diso...
BACKGROUND AND OBJECTIVE: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily a...
There have been many recently published studies exploring machine learning (ML) and deep learning applications within neuroradiology. The improvement ...
BACKGROUND AND PURPOSE: Clinical methods have incomplete diagnostic value for early diagnosis of acute stroke and large vessel occlusion (LVO). Electr...
Artificial intelligence (AI) advancements have significant implications for medical imaging. Stroke is the leading cause of disability and the fifth l...
Frailty, one of the major public health problems in the elderly, can result from multiple etiologic factors including biological and physical changes ...
Hemorrhagic stroke is a medical emergency. Artificial intelligence techniques and algorithms may be used to automatically detect and quantitate intrac...
Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors a...