AIMC Topic: Stroke

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Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.

Sensors (Basel, Switzerland)
Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide expe...

DeepSAP: A Novel Brain Image-Based Deep Learning Model for Predicting Stroke-Associated Pneumonia From Spontaneous Intracerebral Hemorrhage.

Academic radiology
RATIONALE AND OBJECTIVE: Stroke-associated pneumonia (SAP) often appears as a complication following intracerebral hemorrhage (ICH), leading to poor prognosis and increased mortality rates. Previous studies have typically developed prediction models ...

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

IEEE journal of biomedical and health informatics
Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current systems rely on static visual representations for patients to perform motor imagination (MI) tasks, ...

Predicting recovery following stroke: Deep learning, multimodal data and feature selection using explainable AI.

NeuroImage. Clinical
Machine learning offers great potential for automated prediction of post-stroke symptoms and their response to rehabilitation. Major challenges for this endeavour include the very high dimensionality of neuroimaging data, the relatively small size of...

MSE-VGG: A Novel Deep Learning Approach Based on EEG for Rapid Ischemic Stroke Detection.

Sensors (Basel, Switzerland)
Ischemic stroke is a type of brain dysfunction caused by pathological changes in the blood vessels of the brain which leads to brain tissue ischemia and hypoxia and ultimately results in cell necrosis. Without timely and effective treatment in the ea...

Development and validation of an interpretable machine learning model for predicting post-stroke epilepsy.

Epilepsy research
BACKGROUND: Epilepsy is a serious complication after an ischemic stroke. Although two studies have developed prediction model for post-stroke epilepsy (PSE), their accuracy remains insufficient, and their applicability to different populations is unc...

Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers.

Artificial intelligence in medicine
Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varyi...

Identification of endoplasmic reticulum stress genes in human stroke based on bioinformatics and machine learning.

Neurobiology of disease
After ischemic stroke (IS), secondary injury is intimately linked to endoplasmic reticulum (ER) stress and body-brain crosstalk. Nonetheless, the underlying mechanism systemic immune disorder mediated ER stress in human IS remains unknown. In this st...

A machine learning model predicts stroke associated with blood cadmium level.

Scientific reports
Stroke is the leading cause of death and disability worldwide. Cadmium is a prevalent environmental toxicant that may contribute to cardiovascular disease, including stroke. We aimed to build an effective and interpretable machine learning (ML) model...