Neurology

Multiple-Image Deep Learning Analysis for Neuropathy Detection in Corneal Nerve Images.

PURPOSE: Automated classification of corneal confocal images from healthy subjects and diabetic subjects with neuropathy.

Motor imagery classification using geodesic filtering common spatial pattern and filter-bank feature weighted support vector machine.

In recent years, Brain Computer Interface (BCI) based on motor imagery has been widely used in the fields of medicine, active safe systems for automobiles, entertainment, and so on. Motor imagery relevant electroencephalogram (EEG) signals are weak, nonlinear, and susceptible to interference. As a feature extraction method for motor imagery, Common Spatial Pattern (CSP) has been proven to be very ...

Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies-PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice.

In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that th...

Automatic Anterior Chamber Angle Measurement for Ultrasound Biomicroscopy Using Deep Learning.

PURPOSE: To develop a software package for automated measuring of the trabecular-iris angle (TIA) using ultrasound biomicroscopy.

[A comparative study of spinal robot-assisted and traditional fluoroscopy-assisted percutaneous reduction and internal fixation for single-level thoracolumbar fractures without neurological symptoms].

OBJECTIVE: To compare the effectiveness and screw planting accuracy of percutaneous reduction and internal fixation with robot and traditional fluoros...

Using Machine Learning and Structural Neuroimaging to Detect First Episode Psychosis: Reconsidering the Evidence.

Despite the high level of interest in the use of machine learning (ML) and neuroimaging to detect psychosis at the individual level, the reliability o...

Robotic Deep Brain Stimulation (R-DBS)-"Awake" Deep Brain Stimulation Using the Neuromate Robot and O-Arm.

BACKGROUND: Deep brain stimulation (DBS) is an effective surgical technique used to ameliorate the motor symptoms associated with Parkinson's disease....

Predictors of Dementia in the Oldest Old: A Novel Machine Learning Approach.

BACKGROUND: Incidence of dementia increases exponentially with age; little is known about its risk factors in the ninth and 10th decades of life. We i...

Robotic treatment of the upper limb in chronic stroke and cerebral neuroplasticity: a systematic review.

Stroke is the second cause of mortality and the third cause of long-term disability worldwide. Deficits in upper limb (UL) capacity persist at 6 month...

Hand motion analysis during robot-aided rehabilitation in chronic stroke.

A high percentage of post-stroke patients reports spasticity and no functional use of the upper limb. To adapt the therapy in the most patient-specifi...

Hierarchical Structured Sparse Learning for Schizophrenia Identification.

Fractional amplitude of low-frequency fluctuation (fALFF) has been widely used for resting-state functional magnetic resonance imaging (rs-fMRI) based...

3D-Deep Learning Based Automatic Diagnosis of Alzheimer's Disease with Joint MMSE Prediction Using Resting-State fMRI.

We performed this research to 1) evaluate a novel deep learning method for the diagnosis of Alzheimer's disease (AD) and 2) jointly predict the Mini M...

Quadruple Decision Making for Parkinson's Disease Patients: Combining Expert Opinion, Patient Preferences, Scientific Evidence, and Big Data Approaches to Reach Precision Medicine.

Clinical decision making for Parkinson's disease patients is supported by a combination of three distinct information resources: best available scient...

Application of Machine Learning Methods in Predicting Nuclear Receptors and their Families.

Nuclear receptors (NRs) are a superfamily of ligand-dependent transcription factors that are closely related to cell development, differentiation, rep...

Technology-Based Objective Measures Detect Subclinical Axial Signs in Untreated, de novo Parkinson's Disease.

BACKGROUND: Technology-based objective measures (TOMs) recently gained relevance to support clinicians in the assessment of motor function in Parkinso...

Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation.

IMPORTANCE: Interictal epileptiform discharges (IEDs) in electroencephalograms (EEGs) are a biomarker of epilepsy, seizure risk, and clinical decline....

A Machine Learning Approach to Identify a Circulating MicroRNA Signature for Alzheimer Disease.

BACKGROUND: Accurate diagnosis of Alzheimer disease (AD) involving less invasive molecular procedures and at reasonable cost is an unmet medical need....

Upper Limb Robotic Rehabilitation After Stroke: A Multicenter, Randomized Clinical Trial.

BACKGROUND AND PURPOSE: After stroke, only 12% of survivors obtain complete upper limb (UL) functional recovery, while in 30% to 60% UL deficits persi...

Machine Learning Predictive Models Can Improve Efficacy of Clinical Trials for Alzheimer's Disease.

BACKGROUND: The ideal participants for Alzheimer's disease (AD) clinical trials would show cognitive decline in the absence of treatment (i.e., placeb...

Convolutional Neural Network-based MR Image Analysis for Alzheimer's Disease Classification.

BACKGROUND: In this study, we used a convolutional neural network (CNN) to classify Alzheimer's disease (AD), mild cognitive impairment (MCI), and nor...