AIMC Topic: Stroke

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Identification of early invisible acute ischemic stroke in non-contrast computed tomography using two-stage deep-learning model.

Theranostics
Although non-contrast computed tomography (NCCT) is the recommended examination for the suspected acute ischemic stroke (AIS), it cannot detect significant changes in the early infarction. We aimed to develop a deep-learning model to identify early ...

Adjustable Parameters and the Effectiveness of Adjunct Robot-Assisted Gait Training in Individuals with Chronic Stroke.

International journal of environmental research and public health
The aims of this study were (1) to compare the effect of robot-assisted gait orthosis (RAGO) plus conventional physiotherapy with the effect of conventional therapy alone on functional outcomes, including balance, walking ability, muscle strength, da...

SSVEP-Based Brain Computer Interface Controlled Soft Robotic Glove for Post-Stroke Hand Function Rehabilitation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Soft robotic glove with brain computer interfaces (BCI) control has been used for post-stroke hand function rehabilitation. Motor imagery (MI) based BCI with robotic aided devices has been demonstrated as an effective neural rehabilitation tool to im...

Effects of robot-assisted therapy on upper limb and cognitive function in patients with stroke: study protocol of a randomized controlled study.

Trials
BACKGROUND: Impairments in upper limb motor function and cognitive ability are major health problems experienced by stroke patients, necessitating the development of novel and effective treatment options in stroke care. The aim of this study is to ex...

DeepStroke: An efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning.

Medical image analysis
In an emergency room (ER) setting, stroke triage or screening is a common challenge. A quick CT is usually done instead of MRI due to MRI's slow throughput and high cost. Clinical tests are commonly referred to during the process, but the misdiagnosi...

A Machine Learning Model for Predicting Sit-to-Stand Trajectories of People with and without Stroke: Towards Adaptive Robotic Assistance.

Sensors (Basel, Switzerland)
Sit-to-stand and stand-to-sit transfers are fundamental daily motions that enable all other types of ambulation and gait. However, the ability to perform these motions can be severely impaired by different factors, such as the occurrence of a stroke,...

Prediction of Motor Outcome of Stroke Patients Using a Deep Learning Algorithm with Brain MRI as Input Data.

European neurology
BACKGROUND: Deep learning techniques can outperform traditional machine learning techniques and learn from unstructured and perceptual data, such as images and languages. We evaluated whether a convolutional neural network (CNN) model using whole axi...

Stroke Risk Prediction with Machine Learning Techniques.

Sensors (Basel, Switzerland)
A stroke is caused when blood flow to a part of the brain is stopped abruptly. Without the blood supply, the brain cells gradually die, and disability occurs depending on the area of the brain affected. Early recognition of symptoms can significantly...

A Pilot Randomized Controlled Trial of Botulinum Toxin Treatment Combined with Robot-Assisted Therapy, Mirror Therapy, or Active Control Treatment in Patients with Spasticity Following Stroke.

Toxins
Effects of the combined task-oriented trainings with botulinum toxin A (BoNT-A) injection on improving motor functions and reducing spasticity remains unclear. This study aims to investigate effects of 3 task-oriented trainings (robot-assisted therap...