Neurology

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 anesthesia method that can be used to observe the target nerve and its surrounding structures, the puncture needle's advancement and local anesthetics spread in real time. The key in UGNB is nerve identification. With the help of deep learning methods, the automatic identification or segmentation of nerves...

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). The objective of this study was to identify risk factors associated with breakthrough early PTS in TBI patients receiving phenytoin prophylaxis.

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm.

INTRODUCTION: The main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuro...

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 collaborations across biological and physical sciences,...

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 autism, Alzheimer's disease, dementia, cardiovascul...

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

OBJECTIVE: This scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpreta...

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) significantly improved the motor and functional performance o...

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 rehabilitation. However, studies have pointed out...

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 stimulation that would normally support such perception...

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 interface (BCI) based on neurological phenomena ob...

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 required to surpass the learning curve. This study aim...

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 appear during sleep. Large-scale sleep studies woul...

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 importance since the advent of magnetic resonance im...

Classification of attention deficit hyperactivity disorder using machine learning on an EEG dataset.

The neurodevelopmental disorder, Attention Deficit Hyperactivity Disorder (ADHD), frequently affecting youngsters, is characterized by persistent patt...

An emotion recognition method based on EWT-3D-CNN-BiLSTM-GRU-AT model.

This has become a significant study area in recent years because of its use in brain-machine interaction (BMI). The robustness problem of emotion clas...

Quercetin Exerts Anticonvulsant Effect through Mitigation of Neuroinflammatory Response in Pentylenetetrazole-induced Seizure in Mice.

Epilepsy is a chronic disease of the brain characterized by seizures. The currently available anticonvulsants only treat symptoms with serious adverse...

Amplifying pathological detection in EEG signaling pathways through cross-dataset transfer learning.

Pathology diagnosis based on EEG signals and decoding brain activity holds immense importance in understanding neurological disorders. With the advanc...

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing ...

Physicochemical mechanisms of aggregation and fibril formation of α-synuclein and apolipoprotein A-I.

Deposition and accumulation of amyloid fibrils is a hallmark of a group of diseases called amyloidosis and neurodegenerative disorders. Although polyp...

Investigating Activity Recognition for Hemiparetic Stroke Patients Using Wearable Sensors: A Deep Learning Approach with Data Augmentation.

Measuring the daily use of an affected limb after hospital discharge is crucial for hemiparetic stroke rehabilitation. Classifying movements using non...