AIMC Topic: Stroke
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A pilot study for self-guided, active robotic training of proprioception of the upper limb in chronic stroke.
Journal of neuroengineering and rehabilitation
Jun 7, 2025
BACKGROUND: Proprioceptive impairments of the upper limb are common after stroke. These impairments are not typically addressed during assessment or rehabilitation. Currently, most robotic paradigms for training of the upper limb have focused solely ...
Development of a Diagnostic Prediction Model for Post-Stroke Cognitive Impairment in Acute Large Vessel Occlusion Stroke Using Multimodal MRI and PET/CT: A Study Protocol.
Brain and behavior
Jun 1, 2025
OBJECTIVE: Stroke is a leading cause of morbidity and disability worldwide. Post-stroke cognitive impairment (PSCI) significantly affects long-term prognosis in acute anterior circulation large-vessel occlusion stroke (LVO-AIS). This study aims to de...
Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury.
SLAS technology
Jun 1, 2025
According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 to 219/100,000. Continuous care can improve the quality of life of stroke patients and reduce the ra...
Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models.
Artificial intelligence in medicine
Jun 1, 2025
A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical ...
Deep learning-driven multi-class classification of brain strokes using computed tomography: A step towards enhanced diagnostic precision.
European journal of radiology
Jun 1, 2025
OBJECTIVE: To develop and validate deep learning models leveraging CT imaging for the prediction and classification of brain stroke conditions, with the potential to enhance accuracy and support clinical decision-making.
Machine learning algorithms to predict stroke in China based on causal inference of time series analysis.
BMC neurology
May 31, 2025
IMPORTANCE: Identifying and managing high-risk populations for stroke in a targeted manner is a key area of preventive healthcare.
Enhanced neuroplasticity and gait recovery in stroke patients: a comparative analysis of active and passive robotic training modes.
BMC neurology
May 31, 2025
BACKGROUND: Stroke is a leading cause of long-term disability, with lower limb dysfunction being a common sequela that significantly impacts patients' mobility and quality of life. Robotic-assisted training has emerged as a promising intervention for...
Comparison of machine learning models for predicting stroke risk in hypertensive patients: Lasso regression model, random forest model, Boruta algorithm model, and Boruta algorithm combined with Lasso regression model.
Medicine
May 30, 2025
The aim of this study was to compare the performance of 4 machine learning models-Lasso regression model, random forest model, Boruta algorithm model, and the Boruta algorithm combined with Lasso regression-in predicting stroke risk among hypertensiv...
Revolutionizing stroke prediction: a systematic review of AI-powered wearable technologies for early detection of stroke.
Neurosurgical review
May 29, 2025
Wearable technology, combined with artificial intelligence (AI) and machine learning (ML) algorithms, opens up new frontiers for continuously monitoring physiological or behavioural data, allowing the identification of stroke risk factors at an earli...
Stroke prediction in elderly patients with atrial fibrillation using machine learning combined clinical and left atrial appendage imaging phenotypic features.
BMC cardiovascular disorders
May 24, 2025
BACKGROUND: Atrial fibrillation (AF) is one of the primary etiologies for ischemic stroke, and it is of paramount importance to delineate the risk phenotypes among elderly AF patients and to investigate more efficacious models for predicting stroke r...
Support Vector Machine
Age Factors
Predictive Value of Tests
Ischemic Stroke
Aged, 80 and over
Male
Risk Assessment
Incidence
Stroke
Atrial Fibrillation
Computed Tomography Angiography
Atrial Appendage
Humans
Machine Learning
Decision Support Techniques
Phenotype
Female
Risk Factors
Aged
Prospective Studies
Prognosis
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