Strokes

Predicting stroke outcome: A case for multimodal deep learning methods with tabular and CT Perfusion data.

MOTIVATION: Acute ischemic stroke is one of the leading causes of morbidity and disability worldwide, often followed by a long rehabilitation period. To improve and personalize stroke rehabilitation, it is essential to provide a reliable prognosis to caregivers and patients. Deep learning techniques might improve the predictions by incorporating different data modalities. We present a multimodal a...

A metric for characterizing the arm nonuse workspace in poststroke individuals using a robot arm.

An overreliance on the less-affected limb for functional tasks at the expense of the paretic limb and in spite of recovered capacity is an often-observed phenomenon in survivors of hemispheric stroke. The difference between capacity for use and actual spontaneous use is referred to as arm nonuse. Obtaining an ecologically valid evaluation of arm nonuse is challenging because it requires the observ...

Current trends and future perspectives of stroke management through integrating health care team and nanodrug delivery strategy.

Stroke is accounted as the second-most mortality and adult disability factor in worldwide, while causes the bleeding promptly and lifetime consequence...

Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image.

The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. A...

Localization of early infarction on non-contrast CT images in acute ischemic stroke with deep learning approach.

Localization of early infarction on first-line Non-contrast computed tomogram (NCCT) guides prompt treatment to improve stroke outcome. Our previous s...

Safety and Effectiveness of an Anti-Xa-based Unfractionated Heparin Protocol for Impella Percutaneous Ventricular Assist Devices.

Impella devices are used for mechanical circulatory support in patients with cardiogenic shock or those undergoing high-risk percutaneous coronary in...

Effects of high-intensity gait training with and without soft robotic exosuits in people post-stroke: a development-of-concept pilot crossover trial.

INTRODUCTION: High-intensity gait training is widely recognized as an effective rehabilitation approach after stroke. Soft robotic exosuits that enhan...

Chamber Attention Network (CAN): Towards interpretable diagnosis of pulmonary artery hypertension using echocardiography.

INTRODUCTION: Accurate identification of pulmonary arterial hypertension (PAH) in primary care and rural areas can be a challenging task. However, rec...

Identification of Potential Neddylation-related Key Genes in Ischemic Stroke based on Machine Learning Methods.

Ischemic stroke (IS) is a complex neurological disease that can lead to severe disability or even death. Understanding the molecular mechanisms involv...

A Flexible Wearable Supernumerary Robotic Limb for Chronic Stroke Patients.

In this study, we present a flexible wearable supernumerary robotic limb that helps chronic stroke patients with finger rehabilitation and grasping mo...

Soft Robotics to Enhance Upper Limb Endurance in Individuals with Multiple Sclerosis.

Multiple sclerosis (MS) is a chronic autoimmune disorder that affects the central nervous system and can result in various symptoms, including muscle ...

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model.

Both intradialytic hypotension (IDH) and hypertension (IDHTN) are associated with poor outcomes in hemodialysis patients, but a model predicting dual ...

DeepADRA2A: predicting adrenergic α2a inhibitors using deep learning.

Adrenergic α2a (ADRA2A) receptors play a crucial role in modulating various physiological actions, thereby influencing the proper functioning of diffe...

An interpretable ensemble learning model facilitates early risk stratification of ischemic stroke in intensive care unit: Development and external validation of ICU-ISPM.

Ischemic stroke (IS) is a common and severe condition that requires intensive care unit (ICU) admission, with high mortality and variable prognosis. A...

A robot-based interception task to quantify upper limb impairments in proprioceptive and visual feedback after stroke.

BACKGROUND: A key motor skill is the ability to rapidly interact with our dynamic environment. Humans can generate goal-directed motor actions in resp...

Evaluation of a novel real-time adaptive assist-as-needed controller for robot-assisted upper extremity rehabilitation following stroke.

Rehabilitation therapy plays an essential role in assisting people with stroke regain arm function. Upper extremity robot therapy offers a number of a...

The effect of sequential combination of mirror therapy and robot-assisted therapy on motor function, daily function, and self-efficacy after stroke.

Robot-assisted therapy and mirror therapy are both effective in promoting upper limb function after stroke and combining these two interventions might...

Diagnostic test accuracy study of a commercially available deep learning algorithm for ischemic lesion detection on brain MRIs in suspected stroke patients from a non-comprehensive stroke center.

PURPOSE: To estimate the ability of a commercially available artificial intelligence (AI) tool to detect acute brain ischemia on Magnetic Resonance Im...

Non-inferiority of deep learning ischemic stroke segmentation on non-contrast CT within 16-hours compared to expert neuroradiologists.

We determined if a convolutional neural network (CNN) deep learning model can accurately segment acute ischemic changes on non-contrast CT compared to...

CNN-Res: deep learning framework for segmentation of acute ischemic stroke lesions on multimodal MRI images.

BACKGROUND: Accurate segmentation of stroke lesions on MRI images is very important for neurologists in the planning of post-stroke care. Segmentation...