Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI.

BACKGROUND: Parkinson's disease (PD) is the second most common neurodegenerative disease. An objecti...

Network analysis of multimorbidity and health outcomes among persons with spinal cord injury in Canada.

INTRODUCTION: Multimorbidity, defined as the coexistence of two or more health conditions, is common...

Emerging perspectives of synaptic biomarkers in ALS and FTD.

Amyotrophic Lateral Sclerosis (ALS) and Frontotemporal Dementia (FTD) are debilitating neurodegenera...

Role of Artificial Intelligence in Multinomial Decisions and Preventative Nutrition in Alzheimer's Disease.

Alzheimer's disease (AD) affects 50 million people worldwide, an increase of 35 million since 2015, ...

BPSegSys: A Brachial Plexus Nerve Trunk Segmentation System Using Deep Learning.

OBJECTIVE: Ultrasound-guided nerve block anesthesia (UGNB) is a high-tech visual nerve block anesthe...

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS.

INTRODUCTION: Advances in machine learning (ML) methodologies, combined with multidisciplinary colla...

Topological data analysis of the firings of a network of stochastic spiking neurons.

Topological data analysis is becoming more and more popular in recent years. It has found various ap...

Assessing risk factors associated with breakthrough early post-traumatic seizures in patients receiving phenytoin prophylaxis.

OBJECTIVE: Post-traumatic seizure (PTS) is a well-known complication of traumatic brain injury (TBI)...

Enabling Lipidomic Biomarker Studies for Protected Populations by Combining Noninvasive Fingerprint Sampling with MS Analysis and Machine Learning.

Triacylglycerols and wax esters are two lipid classes that have been linked to diseases, including a...

Artificial intelligence for automatic classification of needle EMG signals: A scoping review.

OBJECTIVE: This scoping review provides an overview of artificial intelligence (AI), including machi...

Effects of an assist-as-needed equipped Tenodesis-Induced-Grip Exoskeleton Robot (TIGER) on upper limb function in patients with chronic stroke.

BACKGROUND: The original version of the Tenodesis-Induced-Grip Exoskeleton Robot (TIGER) significant...

NeuroDecodeR: a package for neural decoding in R.

Neural decoding is a powerful method to analyze neural activity. However, the code needed to run a d...

Modelling phenomenological differences in aetiologically distinct visual hallucinations using deep neural networks.

Visual hallucinations (VHs) are perceptions of objects or events in the absence of the sensory stimu...

Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data.

The importance of being physically active and avoiding staying in bed has been recognized in stroke...

EEG-BCI-based motor imagery classification using double attention convolutional network.

This article aims to improve and diversify signal processing techniques to execute a brain-computer ...

Memristor-Based Neuromorphic Chips.

In the era of information, characterized by an exponential growth in data volume and an escalating l...

Challenge in optimizing robotic pancreaticoduodenectomy including nerve plexus hanging maneuver: a Japanese single center experience of 76 cases.

BACKGROUND: Robotic pancreaticoduodenectomy (RPD) is technically demanding, and 20-50 cases are requ...

Multi-pose-based convolutional neural network model for diagnosis of patients with central lumbar spinal stenosis.

Although the role of plain radiographs in diagnosing lumbar spinal stenosis (LSS) has declined in im...

A robust deep learning detector for sleep spindles and K-complexes: towards population norms.

Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive functions that a...

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