Neurology

Joint correction of attenuation and scatter in image space using deep convolutional neural networks for dedicated brain F-FDG PET.

Dedicated brain positron emission tomography (PET) devices can provide higher-resolution images with much lower doses compared to conventional whole-body PET systems, which is important to support PET neuroimaging and particularly useful for the diagnosis of neurodegenerative diseases. However, when a dedicated brain PET scanner does not come with a combined CT or transmission source, there is no ...

Using a machine learning approach to predict outcome after surgery for degenerative cervical myelopathy.

Degenerative cervical myelopathy (DCM) is a spinal cord condition that results in progressive non-traumatic compression of the cervical spinal cord. Spine surgeons must consider a large quantity of information relating to disease presentation, imaging features, and patient characteristics to determine if a patient will benefit from surgery for DCM. We applied a supervised machine learning approach...

Building the drug-GO function network to screen significant candidate drugs for myasthenia gravis.

Myasthenia gravis (MG) is an autoimmune disease. In recent years, considerable evidence has indicated that Gene Ontology (GO) functions, especially GO...

A Supervised Approach to Robust Photoplethysmography Quality Assessment.

Early detection of Atrial Fibrillation (AFib) is crucial to prevent stroke recurrence. New tools for monitoring cardiac rhythm are important for risk ...

An unsupervised neuromorphic clustering algorithm.

Brains perform complex tasks using a fraction of the power that would be required to do the same on a conventional computer. New neuromorphic hardware...

A supervised machine learning approach to characterize spinal network function.

Spontaneous activity is a common feature of immature neuronal networks throughout the central nervous system and plays an important role in network de...

Machine learning based hierarchical classification of frontotemporal dementia and Alzheimer's disease.

BACKGROUND: In a clinical setting, an individual subject classification model rather than a group analysis would be more informative. Specifically, th...

Diagnostic accuracy of frontotemporal dementia. An artificial intelligence-powered study of symptoms, imaging and clinical judgement.

PURPOSE: Frontotemporal dementia (FTD) is a neurodegenerative disorder associated with a poor prognosis and a substantial reduction in quality of life...

Predicting discharge placement after elective surgery for lumbar spinal stenosis using machine learning methods.

PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled...

On the Vulnerability of CNN Classifiers in EEG-Based BCIs.

Deep learning has been successfully used in numerous applications because of its outstanding performance and the ability to avoid manual feature engin...

Deep Learning and Random Forest Approach for Finding the Optimal Traditional Chinese Medicine Formula for Treatment of Alzheimer's Disease.

It has demonstrated that glycogen synthase kinase 3β (GSK3β) is related to Alzheimer's disease (AD). On the basis of the world largest traditional Chi...

A Stroke Risk Detection: Improving Hybrid Feature Selection Method.

BACKGROUND: Stroke is one of the most common diseases that cause mortality. Detecting the risk of stroke for individuals is critical yet challenging b...

Artificial intelligence for understanding concussion: Retrospective cluster analysis on the balance and vestibular diagnostic data of concussion patients.

OBJECTIVES: We propose a bottom-up, machine-learning approach, for the objective vestibular and balance diagnostic data of concussion patients, to pro...

DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal.

BACKGROUND: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians i...

Common spatial pattern and wavelet decomposition for motor imagery EEG- fTCD brain-computer interface.

BACKGROUND: Recently, hybrid brain-computer interfaces (BCIs) combining more than one modality have been investigated with the aim of boosting the per...

Using natural language processing to extract structured epilepsy data from unstructured clinic letters: development and validation of the ExECT (extraction of epilepsy clinical text) system.

OBJECTIVE: Routinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected...

Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: A resting state electroencephalography study.

BACKGROUND: Repetitive transcranial magnetic stimulation (TMS) is clinically effective for major depressive disorder (MDD) and investigational for oth...

Effects of robotic rehabilitation on walking and balance in pediatric patients with hemiparetic cerebral palsy.

BACKGROUND: The most prominent characteristics of hemiparetic cerebral palsy (hCP) children are structural and functional asymmetries. These children ...

DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.

Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yie...

Detection of movement onset using EMG signals for upper-limb exoskeletons in reaching tasks.

BACKGROUND: To assist people with disabilities, exoskeletons must be provided with human-robot interfaces and smart algorithms capable to identify the...