Neurology

A Transferable Deep Learning Prognosis Model for Predicting Stroke Patients' Recovery in Different Rehabilitation Trainings.

Since the underlying mechanisms of neurorehabilitation are not fully understood, the prognosis of stroke recovery faces significant difficulties. Recovery outcomes can vary when undergoing different treatments; however, few models have been developed to predict patient outcomes toward multiple treatments. In this study, we aimed to investigate the potential of predicting a treatment's outcome usin...

Joint mechanical properties estimation with a novel EMG-based knee rehabilitation robot: A machine learning approach.

Joint dynamic properties play essential roles in a wide range of biomechanical movement control. This paper develops a device with a novel mechatronic design to apply small-amplitude perturbations to the human knee. Surface Electromyography is employed to record such information; at the same time, force and position sensors collect measurements to be sent to identify human joint dynamics. For clas...

Predicting Hypoperfusion Lesion and Target Mismatch in Stroke from Diffusion-weighted MRI Using Deep Learning.

Background Perfusion imaging is important to identify a target mismatch in stroke but requires contrast agents and postprocessing software. Purpose To...

Robot-Assisted Rehabilitation Architecture Supported by a Distributed Data Acquisition System.

Rehabilitation robotics aims to facilitate the rehabilitation procedure for patients and physical therapists. This field has a relatively long history...

Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma.

Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to p...

The usability and feasibility validation of the social robot MINI in people with dementia and mild cognitive impairment; a study protocol.

BACKGROUND: Social robots have demonstrated promising outcomes in terms of increasing the social health and well-being of people with dementia and mil...

EMG-driven shared human-robot compliant control for in-hand object manipulation in hand prostheses.

. The limited functionality of hand prostheses remains one of the main reasons behind the lack of its wide adoption by amputees. Indeed, while commerc...

A comparison between robot-guided and stereotactic frame-based stereoelectroencephalography (SEEG) electrode implantation for drug-resistant epilepsy.

The original stereoelectroencephalography frame-based implantation technique has been proven to be safe and effective. But this procedure is complicat...

Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography.

A non-contrast cranial computer tomography (ncCT) is often employed for the diagnosis of the early stage of the ischemic stroke. However, the number o...

Optimization for a New XY Positioning Mechanism by Artificial Neural Network-Based Metaheuristic Algorithms.

This paper devotes a new method in modeling and optimizing to handle the optimization of the XY positioning mechanism. The fitness functions and const...

Robotic-assisted spine surgery allows for increased pedicle screw sizes while still improving safety as indicated by elevated triggered electromyographic thresholds.

The present study used triggered electromyographic (EMG) testing as a tool to determine the safety of pedicle screw placement. In this Institutional R...

VGG-TSwinformer: Transformer-based deep learning model for early Alzheimer's disease prediction.

BACKGROUND AND OBJECTIVE: Mild cognitive impairment (MCI) is a transitional state between normal aging and Alzheimer's disease (AD), and accurately pr...

Neuromorphic Context-Dependent Learning Framework With Fault-Tolerant Spike Routing.

Neuromorphic computing is a promising technology that realizes computation based on event-based spiking neural networks (SNNs). However, fault-toleran...

Centralized and Collective Neurodynamic Optimization Approaches for Sparse Signal Reconstruction via L₁-Minimization.

This article develops several centralized and collective neurodynamic approaches for sparse signal reconstruction by solving the L -minimization probl...

Usability Evaluation of Augmented Reality: A Neuro-Information-Systems Study.

This study introduces an experimental paradigm for a usability test of emerging technologies in a management information system (MIS). The usability t...

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography.

Recently, deep learning-based segmentation models have been widely applied in the ophthalmic field. This study presents the complete process of constr...

Dementia Detection from Speech Using Machine Learning and Deep Learning Architectures.

Dementia affects the patient's memory and leads to language impairment. Research has demonstrated that speech and language deterioration is often a cl...

Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data.

Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present...

Deep learning prediction of stroke thrombus red blood cell content from multiparametric MRI.

BACKGROUND AND PURPOSE: Thrombus red blood cell (RBC) content has been shown to be a significant factor influencing the efficacy of acute ischemic str...

Artificial intelligence techniques for neuropathological diagnostics and research.

Artificial intelligence (AI) research began in theoretical neurophysiology, and the resulting classical paper on the McCulloch-Pitts mathematical neur...