AIMC Topic: Stroke
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Unveiling the potential of machine learning approaches in predicting the emergence of stroke at its onset: a predicting framework.
Scientific reports
Aug 29, 2024
A stroke is a dangerous, life-threatening disease that mostly affects people over 65, but an unhealthy diet is also contributing to the development of strokes at younger ages. Strokes can be treated successfully if they are identified early enough, a...
Assessment of inter-rater and intra-rater reliability of the Luna EMG robot as a tool for assessing upper limb proprioception in patients with stroke-a prospective observational study.
PeerJ
Aug 29, 2024
BACKGROUND: The aim of the study was to assess the inter-rater and intra-rater agreement of measurements performed with the Luna EMG (electromyography) multifunctional robot, a tool for evaluation of upper limb proprioception in individuals with stro...
Discrepancies in ASPECTS obtained by artificial intelligence and experts: Associated factors and prognostic implications.
European journal of radiology
Aug 28, 2024
PURPOSE: The differences between the Alberta Stroke Program Early CT Score (ASPECTS) obtained by experts and artificial intelligence (AI) software require elucidation. We aimed to characterize the discrepancies between the ASPECTS obtained by AI and ...
Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke.
Circulation. Arrhythmia and electrophysiology
Aug 28, 2024
Using Machine Learning to Predict Outcomes Following Transfemoral Carotid Artery Stenting.
Journal of the American Heart Association
Aug 27, 2024
BACKGROUND: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke o...
Digital health in stroke: a narrative review.
Arquivos de neuro-psiquiatria
Aug 26, 2024
Digital health is significantly transforming stroke care, particularly in remote and economically diverse regions, by harnessing mobile and wireless technologies, big data, and artificial intelligence (AI). Despite the promising advancements, a notab...
Machine learning models for outcome prediction in thrombectomy for large anterior vessel occlusion.
Annals of clinical and translational neurology
Aug 23, 2024
OBJECTIVE: Predicting long-term functional outcomes shortly after a stroke is challenging, even for experienced neurologists. Therefore, we aimed to evaluate multiple machine learning models and the importance of clinical/radiological parameters to d...
Establishment and validation of a risk stratification model for stroke risk within three years in patients with cerebral small vessel disease using a combined MRI and machine learning algorithm.
SLAS technology
Aug 21, 2024
BACKGROUND: Cerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity and mortality. Accurate identification of high-risk patients and timing of stroke occurrence plays a...
Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.
Journal of imaging informatics in medicine
Aug 13, 2024
Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, ADC, and FLAIR MRI sequences provide for deep learning in infarct segmentation. Recent technologies in...
Feedback control of heart rate during robotics-assisted tilt table exercise in patients after stroke: a clinical feasibility study.
Journal of neuroengineering and rehabilitation
Aug 12, 2024
BACKGROUND: Patients with neurological disorders including stroke use rehabilitation to improve cognitive abilities, to regain motor function and to reduce the risk of further complications. Robotics-assisted tilt table technology has been developed ...
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