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Stroke

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Brain-Computer Interface-Based Soft Robotic Glove Rehabilitation for Stroke.

IEEE transactions on bio-medical engineering
OBJECTIVE: This randomized controlled feasibility study investigates the ability for clinical application of the Brain-Computer Interface-based Soft Robotic Glove (BCI-SRG) incorporating activities of daily living (ADL)-oriented tasks for stroke reha...

Machine learning to predict mortality after rehabilitation among patients with severe stroke.

Scientific reports
Stroke is among the leading causes of death and disability worldwide. Approximately 20-25% of stroke survivors present severe disability, which is associated with increased mortality risk. Prognostication is inherent in the process of clinical decisi...

Robot Assisted Ankle Neuro-Rehabilitation: State of the art and Future Challenges.

Expert review of neurotherapeutics
: Robot-assisted neuro-rehabilitation is gaining acceptability among the physical therapy community. The ankle is one of the most complicated anatomical joints in the human body and neurologic injuries such as stroke often result in ankle and foot di...

Relationships between motor and cognitive functions and subsequent post-stroke mood disorders revealed by machine learning analysis.

Scientific reports
Mood disorders (e.g. depression, apathy, and anxiety) are often observed in stroke patients, exhibiting a negative impact on functional recovery associated with various physical disorders and cognitive dysfunction. Consequently, post-stroke symptoms ...

Breaking the ice to improve motor outcomes in patients with chronic stroke: a retrospective clinical study on neuromodulation plus robotics.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND: Stroke is one of the main causes of impairment affecting daily activities and quality of life. There is a growing effort to potentiate the recovery of functional gait and to enable stroke patients to walk independently.

Multimodal magnetic resonance imaging correlates of motor outcome after stroke using machine learning.

Neuroscience letters
This study applied machine learning regression to predict motor function after stroke based on multimodal magnetic resonance imaging. Fifty-four stroke patients, who underwent T1 weighted, diffusion tensor, and resting state functional magnetic reson...

Improving Accelerometry-Based Measurement of Functional Use of the Upper Extremity After Stroke: Machine Learning Versus Counts Threshold Method.

Neurorehabilitation and neural repair
BACKGROUND: Wrist-worn accelerometry provides objective monitoring of upper-extremity functional use, such as reaching tasks, but also detects nonfunctional movements, leading to ambiguity in monitoring results.

Automated stroke lesion segmentation in non-contrast CT scans using dense multi-path contextual generative adversarial network.

Physics in medicine and biology
Stroke lesion volume is a key radiologic measurement in assessing prognosis of acute ischemic stroke (AIS) patients. The aim of this paper is to develop an automated segmentation method for accurately segmenting follow-up ischemic and hemorrhagic les...

Tree-Based Machine Learning to Identify and Understand Major Determinants for Stroke at the Neighborhood Level.

Journal of the American Heart Association
Background Stroke is a major cardiovascular disease that causes significant health and economic burden in the United States. Neighborhood community-based interventions have been shown to be both effective and cost-effective in preventing cardiovascul...