Neurology

Brain imaging signatures of neuropathic facial pain derived by artificial intelligence.

Advances in neuroimaging have permitted the non-invasive examination of the human brain in pain. However, a persisting challenge is in the objective differentiation of neuropathic facial pain subtypes, as diagnosis is based on patients' symptom descriptions. We use artificial intelligence (AI) models with neuroimaging data to distinguish subtypes of neuropathic facial pain and differentiate them f...

Application of Deep Learning Algorithm to Monitor Upper Extremity Task Practice.

Upper extremity hemiplegia is a serious problem affecting the lives of many people post-stroke. Motor recovery requires high repetitions and quality of task-specific practice. Sufficient practice cannot be completed during therapy sessions, requiring patients to perform additional task practices at home on their own. Adherence to and quality of these home task practices are often limited, which is...

A Neural Network Approach for Inertial Measurement Unit-Based Estimation of Three-Dimensional Spinal Curvature.

The spine is an important part of the human body. Thus, its curvature and shape are closely monitored, and treatment is required if abnormalities are ...

Deep Learning Models for Stress Analysis in University Students: A Sudoku-Based Study.

Due to the phenomenon of "involution" in China, the current generation of college and university students are experiencing escalating levels of stress...

Classification and deep-learning-based prediction of Alzheimer disease subtypes by using genomic data.

Late-onset Alzheimer's disease (LOAD) is the most common multifactorial neurodegenerative disease among elderly people. LOAD is heterogeneous, and the...

Liquid Metal Flexible EMG Gel Electrodes for Gesture Recognition.

Gesture recognition has been playing an increasingly important role in the field of intelligent control and human-computer interaction. Gesture recogn...

The Utility of Quantitative D-Dimer Assay as a Biomarker in the Diagnosis and Exclusion of Cerebral Venous Sinus Thrombosis.

The utility and sensitivity of quantitative D-Dimer assay to rule out the diagnosis of deep vein thrombosis is well established. We extrapolated this ...

Development of a Deep Learning-Based System for Optic Nerve Characterization in Transorbital Ultrasound Images on a Multicenter Data Set.

OBJECTIVE: Characterization of the optic nerve through measurement of optic nerve diameter (OND) and optic nerve sheath diameter (ONSD) using transorb...

Predicting Surgical Experience After Robotic Nerve-sparing Radical Prostatectomy Simulation Using a Machine Learning-based Multimodal Analysis of Objective Performance Metrics.

INTRODUCTION: Machine learning methods have emerged as objective tools to evaluate operative performance in urological procedures. Our objectives were...

A neuromorphic physiological signal processing system based on VO memristor for next-generation human-machine interface.

Physiological signal processing plays a key role in next-generation human-machine interfaces as physiological signals provide rich cognition- and heal...

Social robotics to support older people with dementia: a study protocol with Paro seal robot in an Italian Alzheimer's day center.

INTRODUCTION: The aging of the population and the high incidence of those over 80 lead to an inevitable increase in chronic degenerative diseases, suc...

EEG-based classification of individuals with neuropsychiatric disorders using deep neural networks: A systematic review of current status and future directions.

The use of deep neural networks for electroencephalogram (EEG) classification has rapidly progressed and gained popularity in recent years, but automa...

A comparison of performance between a deep learning model with residents for localization and classification of intracranial hemorrhage.

Intracranial hemorrhage (ICH) from traumatic brain injury (TBI) requires prompt radiological investigation and recognition by physicians. Computed tom...

An automatic interpretable deep learning pipeline for accurate Parkinson's disease diagnosis using quantitative susceptibility mapping and T1-weighted images.

Parkinson's disease (PD) diagnosis based on magnetic resonance imaging (MRI) is still challenging clinically. Quantitative susceptibility maps (QSM) c...

FCAN-XGBoost: A Novel Hybrid Model for EEG Emotion Recognition.

In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, exis...

A narrative review of the mechanisms and consequences of intermittent hypoxia and the role of advanced analytic techniques in pediatric autonomic disorders.

Disorders of autonomic functions are typically characterized by disturbances in multiple organ systems. These disturbances are often comorbidities of ...

External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study.

OBJECTIVES: To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life...

A Product Fuzzy Convolutional Network for Detecting Driving Fatigue.

Existing driving fatigue detection methods rarely consider how to effectively fuse the advantages of the electroencephalogram (EEG) and electrocardiog...

Source Aware Deep Learning Framework for Hand Kinematic Reconstruction Using EEG Signal.

The ability to reconstruct the kinematic parameters of hand movement using noninvasive electroencephalography (EEG) is essential for strength and endu...

Deep Learning Versus Neurologists: Functional Outcome Prediction in LVO Stroke Patients Undergoing Mechanical Thrombectomy.

BACKGROUND: Despite evolving treatments, functional recovery in patients with large vessel occlusion stroke remains variable and outcome prediction ch...