Neurology

Dual Attention Multi-Instance Deep Learning for Alzheimer's Disease Diagnosis With Structural MRI.

Structural magnetic resonance imaging (sMRI) is widely used for the brain neurological disease diagnosis, which could reflect the variations of brain. However, due to the local brain atrophy, only a few regions in sMRI scans have obvious structural changes, which are highly correlative with pathological features. Hence, the key challenge of sMRI-based brain disease diagnosis is to enhance the iden...

Deep learning multimodal fNIRS and EEG signals for bimanual grip force decoding.

Non-invasive brain-machine interfaces (BMIs) offer an alternative, safe and accessible way to interact with the environment. To enable meaningful and stable physical interactions, BMIs need to decode forces. Although previously addressed in the unimanual case, controlling forces from both hands would enable BMI-users to perform a greater range of interactions. We here investigate the decoding of h...

Efficacy of wrist robot-aided orthopedic rehabilitation: a randomized controlled trial.

BACKGROUND: In recent years, many studies focused on the use of robotic devices for both the assessment and the neuro-motor reeducation of upper limb ...

Detection of dementia on voice recordings using deep learning: a Framingham Heart Study.

BACKGROUND: Identification of reliable, affordable, and easy-to-use strategies for detection of dementia is sorely needed. Digital technologies, such ...

Robot-Assisted Gait Training Plan for Patients in Poststroke Recovery Period: A Single Blind Randomized Controlled Trial.

BACKGROUND: Walking dysfunction exists in most patients after stroke. Evidence regarding gait training in two weeks is scarce in resource-limited sett...

Fused CNN-LSTM deep learning emotion recognition model using electroencephalography signals.

The traditional machine learning-based emotion recognition models have shown effective performance for classifying Electroencephalography (EEG) based...

DeepAtrophy: Teaching a neural network to detect progressive changes in longitudinal MRI of the hippocampal region in Alzheimer's disease.

Measures of change in hippocampal volume derived from longitudinal MRI are a well-studied biomarker of disease progression in Alzheimer's disease (AD)...

Development of a machine learning-based real-time location system to streamline acute endovascular intervention in acute stroke: a proof-of-concept study.

BACKGROUND: Delivery of acute stroke endovascular intervention can be challenging because it requires complex coordination of patient and staff across...

Quantifying changes over 1 year in motor and cognitive skill after transient ischemic attack (TIA) using robotics.

Recent work has highlighted that people who have had TIA may have abnormal motor and cognitive function. We aimed to quantify deficits in a cohort of ...

Exoskeleton end-effector robot-assisted therapy for finger-hand motor recovery in stroke survivors: systematic review and meta-analysis.

INTRODUCTION: The growing number of stroke survivors with residual hand disabilities requires the development of efficient recovery therapy, and robot...

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data.

Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. ...

Deep learning assisted quantitative assessment of histopathological markers of Alzheimer's disease and cerebral amyloid angiopathy.

Traditionally, analysis of neuropathological markers in neurodegenerative diseases has relied on visual assessments of stained sections. Resulting sem...

Strategies for feature extraction from structural brain imaging in lesion-deficit modelling.

High-dimensional modelling of post-stroke deficits from structural brain imaging is highly relevant to basic cognitive neuroscience and bears the pote...

Generalized Deep Learning EEG Models for Cross-Participant and Cross-Task Detection of the Vigilance Decrement in Sustained Attention Tasks.

Tasks which require sustained attention over a lengthy period of time have been a focal point of cognitive fatigue research for decades, with these ta...

Robot-assisted stereotactic multiple brain abscesses' puncture: technical case report.

We report a case of multiple brain abscesses' puncture, employing the ROSA™ Brain surgical robot (Zimmer Biomet) and the O-arm® O2 Imaging System (Med...

Improving cerebral microvascular image quality of optical coherence tomography angiography with deep learning-based segmentation.

Optical coherence tomography angiography (OCTA) can map the microvascular networks of the cerebral cortices with micrometer resolution and millimeter ...

Random walks on B distributed resting-state functional connectivity to identify Alzheimer's disease and Mild Cognitive Impairment.

OBJECTIVE: Resting-state functional connectivity reveals a promising way for the early detection of dementia. This study proposes a novel method to ac...

Robot Assisted Distal Pancreatectomy with Celiac Axis Resection (DP-CAR) for Pancreatic Cancer: Surgical Planning and Technique.

Malignant pancreatic tumors involving the celiac artery can be resected with a distal pancreatectomy, splenectomy and celiac axis resection (DP-CAR), ...

Accounting for symptom heterogeneity can improve neuroimaging models of antidepressant response after electroconvulsive therapy.

Depression symptom heterogeneity limits the identifiability of treatment-response biomarkers. Whether improvement along dimensions of depressive sympt...

A deep learning method for single-trial EEG classification in RSVP task based on spatiotemporal features of ERPs.

. Single-trial electroencephalography (EEG) classification is of great importance in the rapid serial visual presentation (RSVP) task. Convolutional n...