Neurology

Automatic Extraction of Risk Factors for Dialysis Patients from Clinical Notes Using Natural Language Processing Techniques.

Studies have shown that mental health and comorbidities such as dementia, diabetes and cardiovascular diseases are risk factors for dialysis patients. Extracting accurate and timely information associated with these risk factors in the patient health records is not only important for dialysis patient management, but also for real-world evidence generation. We presented HERALD, an natural language ...

Digitalisation of the Brief Visuospatial Memory Test-Revised and Evaluation with a Machine Learning Algorithm.

The disease multiple sclerosis (MS) is characterized by various neurological symptoms. This paper deals with a novel tool to assess cognitive dysfunction. The Brief Visuospatial Memory Test-Revised (BVMT-R) is a recognized method to measure optical recognition deficits and their progression. Typically, the test is carried out on paper. We present a way to make this process more efficient, without ...

Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.

Identifying subtypes of Alzheimer's Disease (AD) can lead towards the creation of personalized interventions and potentially improve outcomes. In this...

The Smart Device System for Movement Disorders: Preliminary Evaluation of Diagnostic Accuracy in a Prospective Study.

Consumer wearables can provide objective monitoring of movement disorders and may identify new phenotypical biomarkers. We present a novel smartwatch-...

Automated Nerve Fibres Identification and Morphometry Analysis with Neural Network Based Tool in MATLAB.

Analyses of nerve histology are core assays in basic and applied research and even in clinical setting. Detailed report on nerve morphology may unbias...

Porthole and Stormcloud: Tools for Visualisation of Spatiotemporal M/EEG Statistics.

Electro- and magneto-encephalography are functional neuroimaging modalities characterised by their ability to quantify dynamic spatiotemporal activity...

Emerging technologies in neuromuscular ultrasound.

Neuromuscular ultrasound is an accepted and valuable element in the evaluation of peripheral nerve and muscle disease. However, ultrasound has several...

Effects of trunk stabilization training robot on postural control and gait in patients with chronic stroke: a randomized controlled trial.

Our study aimed to confirm the therapeutic effects of using a trunk stabilization training robot (3DBT-33) in patients with chronic stroke. A total of...

The future of upper extremity rehabilitation robotics: research and practice.

The loss of upper limb motor function can have a devastating effect on people's lives. To restore upper limb control and functionality, researchers an...

Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process.

In the context of neuro-pathological disorders, neuroimaging has been widely accepted as a clinical tool for diagnosing patients with Alzheimer's dise...

Mantis-ml: Disease-Agnostic Gene Prioritization from High-Throughput Genomic Screens by Stochastic Semi-supervised Learning.

Access to large-scale genomics datasets has increased the utility of hypothesis-free genome-wide analyses. However, gene signals are often insufficien...

Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach.

BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a l...

Robot-assisted stereoelectroencephalography exploration of the limbic thalamus in human focal epilepsy: implantation technique and complications in the first 24 patients.

OBJECTIVE: Despite numerous imaging studies highlighting the importance of the thalamus in a patient's surgical prognosis, human electrophysiological ...

Role of Instruction Adherence During Highly Structured Robotic Arm Training on Motor Outcomes for Individuals After Chronic Stroke.

The aim of this study was to examine the effects of instruction adherence on upper limb motor outcomes after highly structured intervention. A seconda...

Assessment of a Segmentation-Free Deep Learning Algorithm for Diagnosing Glaucoma From Optical Coherence Tomography Scans.

IMPORTANCE: Conventional segmentation of the retinal nerve fiber layer (RNFL) is prone to errors that may affect the accuracy of spectral-domain optic...

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques.

Electroencephalography is the recording of brain electrical activities that can be used to diagnose brain seizure disorders. By identifying brain acti...

Factors affecting the usability of an assistive soft robotic glove after stroke or multiple sclerosis.

OBJECTIVE: To explore the usability and effects of an assistive soft robotic glove in the home setting after stroke or multiple sclerosis.

Two distinct neuroanatomical subtypes of schizophrenia revealed using machine learning.

Neurobiological heterogeneity in schizophrenia is poorly understood and confounds current analyses. We investigated neuroanatomical subtypes in a mult...

Robot-Assisted Insular Depth Electrode Implantation Through Oblique Trajectories: 3-Dimensional Anatomical Nuances, Technique, Accuracy, and Safety.

BACKGROUND: The insula is a deep cortical structure that has renewed interest in epilepsy investigation. Invasive EEG recordings of this region have b...

Neuropsychiatric symptoms as predictors of conversion from MCI to dementia: a machine learning approach.

OBJECTIVES: To use a Machine Learning (ML) approach to compare Neuropsychiatric Symptoms (NPS) in participants of a longitudinal study who developed d...