Neurology

Deep learning signature of brain [F]FDG PET associated with cognitive outcome of rapid eye movement sleep behavior disorder.

An objective biomarker to predict the outcome of isolated rapid eye movement sleep behavior disorder (iRBD) is crucial for the management. This study aimed to investigate cognitive signature of brain [F]FDG PET based on deep learning (DL) for evaluating patients with iRBD. Fifty iRBD patients, 19 with mild cognitive impairment (MCI) (RBD-MCI) and 31 without MCI (RBD-nonMCI), were prospectively enr...

3DCANN: A Spatio-Temporal Convolution Attention Neural Network for EEG Emotion Recognition.

Since electroencephalogram (EEG) signals can truly reflect human emotional state, emotion recognition based on EEG has turned into a critical branch in the field of artificial intelligence. Aiming at the disparity of EEG signals in various emotional states, we propose a new deep learning model named three-dimension convolution attention neural network (3DCANN) for EEG emotion recognition in this p...

Multi-Scale Deep Learning of Clinically Acquired Multi-Modal MRI Improves the Localization of Seizure Onset Zone in Children With Drug-Resistant Epilepsy.

The present study investigates the effectiveness of a deep learning neural network for non-invasively localizing the seizure onset zone (SOZ) using mu...

Emotion recognition while applying cosmetic cream using deep learning from EEG data; cross-subject analysis.

We report a deep learning-based emotion recognition method using EEG data collected while applying cosmetic creams. Four creams with different texture...

Multi-label classification of Alzheimer's disease stages from resting-state fMRI-based correlation connectivity data and deep learning.

Alzheimer's disease is a neurodegenerative condition that gradually impairs cognitive abilities. Recently, various neuroimaging modalities and machine...

NerveStitcher: Corneal confocal microscope images stitching with neural networks.

Corneal nerves are of great interest to clinicians and scientists due to their potential for the diagnosis of early neurological disorders. In vivo co...

Utilizing machine learning algorithms to predict subject genetic mutation class from in silico models of neuronal networks.

BACKGROUND: Epilepsy is the fourth-most common neurological disorder, affecting an estimated 50 million patients globally. Nearly 40% of patients have...

Fuzzy inference system (FIS) - long short-term memory (LSTM) network for electromyography (EMG) signal analysis.

A wide range of application domains,s such as remote robotic control, rehabilitation, and remote surgery, require capturing neuromuscular activities. ...

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.

BACKGROUND: The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases...

The Deep Learning Method Differentiates Patients with Bipolar Disorder from Controls with High Accuracy Using EEG Data.

Bipolar disorder (BD) is a mental disorder characterized by depressive and manic or hypomanic episodes. The complexity in the diagnosis of Bipolar di...

Detection of increased intracranial pressure in trans-oral robotic thyroidectomy using optic nerve sheath diameter measurement.

BACKGROUND: During transoral robot-assisted thyroidectomy, there is a risk of increasing intracranial pressure because the site of CO insufflation is ...

Transcranial Doppler analysis based on computer and artificial intelligence for acute cerebrovascular disease.

Cerebrovascular disease refers to damage to brain tissue caused by impaired intracranial blood circulation. It usually presents clinically as an acute...

A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal.

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with autom...

M1M2: Deep-Learning-Based Real-Time Emotion Recognition from Neural Activity.

Emotion recognition, or the ability of computers to interpret people's emotional states, is a very active research area with vast applications to impr...

Being watched by a humanoid robot and a human: Effects on affect-related psychophysiological responses.

Eye contact with a humanoid robot has been shown to evoke similar affect and affiliation related psychophysiological responses as eye contact with ano...

Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements.

PURPOSE: To estimate central 10-degree visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thic...

Strategies to Implement Pet Robots in Long-Term Care Facilities for Dementia Care: A Modified Delphi Study.

OBJECTIVES: Pet robots are technology-based substitutes for live animals that have demonstrated psychosocial benefits for people living with dementia ...

A novel combination of corneal confocal microscopy, clinical features and artificial intelligence for evaluation of ocular surface pain.

OBJECTIVES: To analyse various corneal nerve parameters using confocal microscopy along with systemic and orthoptic parameters in patients presenting ...

Machine Learning and Prediction in Fetal, Infant, and Toddler Neuroimaging: A Review and Primer.

Predictive models in neuroimaging are increasingly designed with the intent to improve risk stratification and support interventional efforts in psych...

Intraoperative angiography of the neurovascular bundle using indocyanine green and near-infrared fluorescence improves anatomical dissection during robot-assisted radical prostatectomy: initial clinical experience.

Landmark artery identification in the neurovascular bundle (NVB) is important for nerve-sparing in radical prostatectomy. We aimed to investigate intr...