Neurology

Deep-learning-based seizure detection and prediction from electroencephalography signals.

Electroencephalography (EEG) is among the main tools used for analyzing and diagnosing epilepsy. The manual analysis of EEG must be conducted by highly trained clinicians or neuro-physiologists; a process that is considered to have a comparatively low inter-rater agreement. Furthermore, the new data interpretation consumes an excessive amount of time and resources. Hence, an automatic seizure dete...

A large intrathoracic goiter with tracheal stenosis: Complete resection using a robot-assisted thoracoscopic approach.

Growing intrathoracic goiters may compress surrounding organs and deteriorate the cardiopulmonary function. Treating such cases requires carefully considering how to maintain oxygenation and resect the tumor with minimal invasiveness without complications. We herein report a surgically resected case of a large intrathoracic goiter-compressed trachea extending from the right lower pole of the thyro...

A deep learning-based multisite neuroimage harmonization framework established with a traveling-subject dataset.

The accumulation of multisite large-sample MRI datasets collected during large brain research projects in the last decade has provided critical resour...

A neuroscience-inspired spiking neural network for EEG-based auditory spatial attention detection.

Recent studies have shown that alpha oscillations (8-13 Hz) enable the decoding of auditory spatial attention. Inspired by sparse coding in cortical n...

A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models.

Upper limb amputation severely affects the quality of life and the activities of daily living of a person. In the last decade, many robotic hand prost...

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions.

BACKGROUND: Generalized regression neural network (GRNN) and logistic regression (LR) are extensively used in the medical field; however, the better m...

Effects of Robot-Assisted Gait Training with Body Weight Support on Gait and Balance in Stroke Patients.

This study investigated the effects of robot-assisted gait training with body weight support on gait and balance in stroke patients. The study partici...

Deep learning-based brain age prediction in normal aging and dementia.

Brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has ...

Early Diagnosis of Acute Ischemic Stroke by Brain Computed Tomography Perfusion Imaging Combined with Head and Neck Computed Tomography Angiography on Deep Learning Algorithm.

The purpose of the research was to discuss the application values of deep learning algorithm-based computed tomography perfusion (CTP) imaging combine...

Deep Learning-Based Computed Tomography Perfusion Imaging to Evaluate the Effectiveness and Safety of Thrombolytic Therapy for Cerebral Infarct with Unknown Time of Onset.

This study was aimed to discuss the effectiveness and safety of deep learning-based computed tomography perfusion (CTP) imaging in the thrombolytic th...

Demystifying the Black Box: The Importance of Interpretability of Predictive Models in Neurocritical Care.

Neurocritical care patients are a complex patient population, and to aid clinical decision-making, many models and scoring systems have previously bee...

The effect of robotic rehabilitation on posture and trunk control in non-ambulatory cerebral palsy.

The purpose of this study was to investigate the effects of a combined robot-assisted gait training (RAGT) with standard physiotherapy (PT) on trunk c...

Cluster-Then-Classify Methodology for the Identification of Pain Episodes in Chronic Diseases.

Chronic diseases benefit of the advances on personalize medicine coming out of the integrative convergence of significant developments in systems biol...

Deep-Learning-Assisted Stratification of Amyloid Beta Mutants Using Drying Droplet Patterns.

The development of simple and accurate methods to predict mutations in proteins remains an unsolved challenge in modern biochemistry. It is discovered...

Leveraging machine learning tools and algorithms for analysis of fruit fly morphometrics.

Analysis of landmark-based morphometric measurements taken on body parts of insects have been a useful taxonomic approach alongside DNA barcoding in i...

Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes.

IMPORTANCE: The entry of artificial intelligence into medicine is pending. Several methods have been used for the predictions of structured neuroimagi...

Using EMG signals to assess proximity of instruments to nerve roots during robot-assisted spinal surgery.

BACKGROUND: Detecting neural threats using electromyography (EMG) has gained recognition in the field of spinal surgery. To provide an efficient appro...

Reporting details of neuroimaging studies on individual traits prediction: A literature survey.

Using machine-learning tools to predict individual phenotypes from neuroimaging data is one of the most promising and hence dynamic fields in systems ...

Brain network connectivity feature extraction using deep learning for Alzheimer's disease classification.

Early diagnosis and therapeutic intervention for Alzheimer's disease (AD) is currently the only viable option for improving clinical outcomes. Combini...

Memristive Circuit Implementation of a Self-Repairing Network Based on Biological Astrocytes in Robot Application.

A large number of studies have shown that astrocytes can be combined with the presynaptic terminals and postsynaptic spines of neurons to constitute a...