Neurology

AI Therapist Realizing Expert Verbal Cues for Effective Robot-Assisted Gait Training.

Repetitive and specific verbal cues by a therapist are essential in aiding a patient's motivation and improving the motor learning process. The verbal cues comprise various expressions, sentences, volumes, and timings, depending on the therapist's proficiency. This paper proposes an AI therapist (AI-T) that implements the verbal cues of professional therapists having extensive experience with robo...

The Metabolic Cost of Exercising With a Robotic Exoskeleton: A Comparison of Healthy and Neurologically Impaired People.

While neuro-recovery is maximized through active engagement, it has been suggested that the use of robotic exoskeletons in neuro-rehabilitation provides passive therapy. Using oxygen consumption (VO) as an indicator of energy expenditure, we investigated the metabolic requirements of completing exercises in a free-standing robotic exoskeleton, with 20 healthy and 12 neurologically impaired partici...

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in -derived astrocytes ablated mice.

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) work...

Promises and pitfalls of deep neural networks in neuroimaging-based psychiatric research.

By promising more accurate diagnostics and individual treatment recommendations, deep neural networks and in particular convolutional neural networks ...

Clinical outcome prediction from analysis of microelectrode recordings using deep learning in subthalamic deep brain stimulation for Parkinson`s disease.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is an effective treatment for improving the motor symptoms of advanced Parki...

High resolution automated labeling of the hippocampus and amygdala using a 3D convolutional neural network trained on whole brain 700 μm isotropic 7T MP2RAGE MRI.

Image labeling using convolutional neural networks (CNNs) are a template-free alternative to traditional morphometric techniques. We trained a 3D deep...

Systematic review of guidelines to identify recommendations for upper limb robotic rehabilitation after stroke.

INTRODUCTION: Upper limb motor impairment is one of the most frequent stroke consequences. Robot therapy may represent a valid option for upper limb s...

Screening of Alzheimer's disease by facial complexion using artificial intelligence.

Despite the increasing incidence and high morbidity associated with dementia, a simple, non-invasive, and inexpensive method of screening for dementia...

Building an Automated Orofacial Pain, Headache and Temporomandibular Disorder Diagnosis System.

Physicians collect data in patient encounters that they use to diagnose patients. This process can fail if the needed data is not collected or if phys...

Machine Learning Techniques for Personalized Detection of Epileptic Events in Clinical Video Recordings.

Continuous patient monitoring is essential to achieve an effective and optimal patient treatment in the intensive care unit. In the specific case of e...

Brain Asymmetry Detection and Machine Learning Classification for Diagnosis of Early Dementia.

Early identification of degenerative processes in the human brain is considered essential for providing proper care and treatment. This may involve de...

Parkinson's disease: deep learning with a parameter-weighted structural connectome matrix for diagnosis and neural circuit disorder investigation.

PURPOSE: To investigate whether Parkinson's disease (PD) can be differentiated from healthy controls and to identify neural circuit disorders in PD by...

Cognitive and MRI trajectories for prediction of Alzheimer's disease.

The concept of Mild Cognitive Impairment (MCI) is used to describe the early stages of Alzheimer's disease (AD), and identification and treatment befo...

A Review on Machine Learning for EEG Signal Processing in Bioengineering.

Electroencephalography (EEG) has been a staple method for identifying certain health conditions in patients since its discovery. Due to the many diffe...

Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review.

With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques...

Modeling autosomal dominant Alzheimer's disease with machine learning.

INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease.

Na MRI in ischemic stroke: Acquisition time reduction using postprocessing with convolutional neural networks.

Quantitative Na magnetic resonance imaging (MRI) provides tissue sodium concentration (TSC), which is connected to cell viability and vitality. Long ...

Robot enhanced stroke therapy optimizes rehabilitation (RESTORE): a pilot study.

BACKGROUND: Robotic rehabilitation after stroke provides the potential to increase and carefully control dosage of therapy. Only a small number of stu...

A combined convolutional and recurrent neural network for enhanced glaucoma detection.

Glaucoma, a leading cause of blindness, is a multifaceted disease with several patho-physiological features manifesting in single fundus images (e.g.,...

Clinical value of machine learning-based interpretation of I-123 FP-CIT scans to detect Parkinson's disease: a two-center study.

PURPOSE: Our aim was to develop and validate a machine learning (ML)-based approach for interpretation of I-123 FP-CIT SPECT scans to discriminate Par...