Latest AI and machine learning research in neurology for healthcare professionals.
Tactile perception enabled by somatosensory system in human is essential for dexterous tool usage, communication, and interaction. Imparting tactile recognition functions to advanced robots and interactive systems can potentially improve their cognition and intelligence. Here, a flexible artificial sensory nerve that mimics the tactile sensing, neural coding, and synaptic processing functions in h...
OBJECTIVES: We proposed a novel deep learning-based radiomics (DLR) model to diagnose Parkinson's disease (PD) based on [F]fluorodeoxyglucose (FDG) PET images.
BACKGROUND: The EEG-based emotion recognition is one of the primary research orientations in the field of emotional intelligence and human-computer in...
Assistive robotic manipulators (ARMs) provide a potential solution to mitigating the difficulties and lost independence associated with manipulation d...
INTRODUCTION: To test the utility of the "A/T/N" system in the Chinese population, we study core Alzheimer's disease (AD) biomarkers in a newly establ...
BACKGROUND: Stroke is a leading cause of serious gait impairments and restoring walking ability is a major goal of physical therapy interventions. Sof...
BACKGROUND: Stroke related motor function deficits affect patients' likelihood of returning to professional activities, limit their participation in s...
Concussions, also known as mild traumatic brain injury (mTBI), are a growing health challenge. Approximately four million concussions are diagnosed an...
Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons' differentiation by skill ...
OBJECTIVES: Neuropsychological tests (NPTs) are widely used tools to evaluate cognitive functioning. The interpretation of these tests can be time-con...
Automatic extraction of cerebral vessels and cranial nerves has important clinical value in the treatment of trigeminal neuralgia (TGN) and hemifacial...
Elimination of intra-artifacts in EEG has been overlooked in most of the existing sleep staging systems, especially in deep learning-based approaches....
Accurate identification and localization of the vertebrae in CT scans is a critical and standard pre-processing step for clinical spinal diagnosis and...
OBJECTIVE: To develop a fusion model combining clinical variables, deep learning (DL), and radiomics features to predict the functional outcomes early...
This study was aimed at exploring the application value of electroacupuncture in the treatment of peripheral facial palsy using surface nerve electrom...
Stroke can be a devastating condition that impairs the upper limb and reduces mobility. Wearable robots can aid impaired users by supporting performan...
Electroencephalography (EEG) is widely used for mental stress classification, but effective feature extraction and transfer across subjects remain cha...
BACKGROUND: The use of multi-modal features for improving the diagnosing accuracy of Parkinson's disease (PD) is still under consideration.
BACKGROUNDS: We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchm...
BACKGROUND: Pet robots have been employed as viable substitutes to pet therapy in nursing homes. Despite their potential to enhance the psychosocial h...