Neurology

Identifying neurocognitive disorder using vector representation of free conversation.

In recent years, studies on the use of natural language processing (NLP) approaches to identify dementia have been reported. Most of these studies used picture description tasks or other similar tasks to encourage spontaneous speech, but the use of free conversation without requiring a task might be easier to perform in a clinical setting. Moreover, free conversation is unlikely to induce a learni...

Multi-Task Weakly-Supervised Attention Network for Dementia Status Estimation With Structural MRI.

Accurate prediction of clinical scores (of neuropsychological tests) based on noninvasive structural magnetic resonance imaging (MRI) helps understand the pathological stage of dementia (e.g., Alzheimer's disease (AD)) and forecast its progression. Existing machine/deep learning approaches typically preselect dementia-sensitive brain locations for MRI feature extraction and model construction, pot...

Antistroke Network Pharmacological Prediction of Xiaoshuan Tongluo Recipe Based on Drug-Target Interaction Based on Deep Learning.

Stroke is a common cerebrovascular disease that threatens human health, and the search for therapeutic drugs is the key to treatment. New drug discove...

Deep-learning prediction of amyloid deposition from early-phase amyloid positron emission tomography imaging.

OBJECTIVE: While the use of biomarkers for the detection of early and preclinical Alzheimer's Disease has become essential, the need to wait for over ...

Tissue Recognition Based on Electrical Impedance Classified by Support Vector Machine in Spinal Operation Area.

OBJECTIVE: One of the major difficulties in spinal surgery is the injury of important tissues caused by tissue misclassification, which is the source ...

DeepGrading: Deep Learning Grading of Corneal Nerve Tortuosity.

Accurate estimation and quantification of the corneal nerve fiber tortuosity in corneal confocal microscopy (CCM) is of great importance for disease u...

Adaptive wireless millirobotic locomotion into distal vasculature.

Microcatheters have enabled diverse minimally invasive endovascular operations and notable health benefits compared with open surgeries. However, with...

Computer-aided extraction of select MRI markers of cerebral small vessel disease: A systematic review.

Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most ...

A deep learning framework for epileptic seizure detection based on neonatal EEG signals.

Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human exper...

Unsupervised Deep Learning for Stroke Lesion Segmentation on Follow-up CT Based on Generative Adversarial Networks.

BACKGROUND AND PURPOSE: Supervised deep learning is the state-of-the-art method for stroke lesion segmentation on NCCT. Supervised methods require man...

Prediction of Metabolic Characteristics of Cardiovascular and Cerebrovascular Diseases Based on Convolutional Neural Network.

As a typical disease, cardiovascular and cerebrovascular diseases cause great damage to the human body. In view of the problem that the existing model...

Deep neural networks constrained by neural mass models improve electrophysiological source imaging of spatiotemporal brain dynamics.

Many efforts have been made to image the spatiotemporal electrical activity of the brain with the purpose of mapping its function and dysfunction as w...

Effect of Robot-Assisted Training on Unilateral Spatial Neglect After Stroke: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Several studies have shown that robotic devices can effectively improve motor function in stroke patients through limb activation. However...

Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks.

The paper is devoted to the study of EEG-based people verification. Analyzed solutions employed shallow artificial neural networks using spectral EEG ...

Relation between Cortical Activation and Effort during Robot-Mediated Walking in Healthy People: A Functional Near-Infrared Spectroscopy Neuroimaging Study (fNIRS).

Force and effort are important components of a motor task that can impact rehabilitation effectiveness. However, few studies have evaluated the impact...

Towards the interpretability of deep learning models for multi-modal neuroimaging: Finding structural changes of the ageing brain.

Brain-age (BA) estimates based on deep learning are increasingly used as neuroimaging biomarker for brain health; however, the underlying neural featu...

Development of a machine learning-based risk prediction model for cerebral infarction and comparison with nomogram model.

BACKGROUND: Development of a cerebral infarction (CI) risk prediction model by mining routine test big data with machine learning algorithms.

Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.

Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of mov...

Morphological Fabrication of Equilibrium and Auditory Sensors through Electrolytic Polymerization on Hybrid Fluid Rubber (HF Rubber) for Smart Materials of Robotics.

The development of auditory sensors and systems is essential in smart materials of robotics and is placed at the strategic category of mutual communic...

Classification and Reconstruction of Biomedical Signals Based on Convolutional Neural Network.

The efficient biological signal processing method can effectively improve the efficiency of researchers to explore the work of life mechanism, so as t...