AIMC Topic: Brain Ischemia
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Automated Collateral Flow Assessment in Patients with Acute Ischemic Stroke Using Computed Tomography with Artificial Intelligence Algorithms.
World neurosurgery
Sep 16, 2021
BACKGROUND: Collateral circulation is associated with improved functional outcome in patients with large vessel occlusion acute ischemic stroke (AIS) who undergo reperfusion therapy. Assessment of collateral flow can be time consuming, subjective, an...
Machine learning models of ischemia/hemorrhage in moyamoya disease and analysis of its risk factors.
Clinical neurology and neurosurgery
Aug 30, 2021
OBJECT: This study aimed to determine the risk factors of ischemic/hemorrhagic stroke in patients suffering moyamoya disease (MMD), as well as to compare the effects of six analysis methods.
AIFNet: Automatic vascular function estimation for perfusion analysis using deep learning.
Medical image analysis
Aug 6, 2021
Perfusion imaging is crucial in acute ischemic stroke for quantifying the salvageable penumbra and irreversibly damaged core lesions. As such, it helps clinicians to decide on the optimal reperfusion treatment. In perfusion CT imaging, deconvolution ...
Deep learning-based identification of acute ischemic core and deficit from non-contrast CT and CTA.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Jun 8, 2021
The accurate identification of irreversible infarction and salvageable tissue is important in planning the treatments for acute ischemic stroke (AIS) patients. Computed tomographic perfusion (CTP) can be used to evaluate the ischemic core and deficit...
Ischemic stroke-induced polyaxonal innervation at the neuromuscular junction is attenuated by robot-assisted mechanical therapy.
Experimental neurology
May 25, 2021
Ischemic stroke is a leading cause of disability world-wide. Mounting evidence supports neuromuscular pathology following stroke, yet mechanisms of dysfunction and therapeutic action remain undefined. The objectives of our study were to investigate n...
Predicting Infarct Core From Computed Tomography Perfusion in Acute Ischemia With Machine Learning: Lessons From the ISLES Challenge.
Stroke
May 7, 2021
BACKGROUND AND PURPOSE: The ISLES challenge (Ischemic Stroke Lesion Segmentation) enables globally diverse teams to compete to develop advanced tools for stroke lesion analysis with machine learning. Detection of irreversibly damaged tissue on comput...
Intra-domain task-adaptive transfer learning to determine acute ischemic stroke onset time.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 24, 2021
Treatment of acute ischemic strokes (AIS) is largely contingent upon the time since stroke onset (TSS). However, TSS may not be readily available in up to 25% of patients with unwitnessed AIS. Current clinical guidelines for patients with unknown TSS...
Tissue at Risk and Ischemic Core Estimation Using Deep Learning in Acute Stroke.
AJNR. American journal of neuroradiology
Mar 25, 2021
BACKGROUND AND PURPOSE: In acute stroke patients with large vessel occlusions, it would be helpful to be able to predict the difference in the size and location of the final infarct based on the outcome of reperfusion therapy. Our aim was to demonstr...
The Influence of EMG-Triggered Robotic Movement on Walking, Muscle Force and Spasticity after an Ischemic Stroke.
Medicina (Kaunas, Lithuania)
Mar 2, 2021
: Application of the EMG-driven robotic training in everyday therapeutic processes is a modern and innovative form of neurorehabilitation among patients after stroke. Active participation of the patient contributes to significantly higher activation ...
Systematic review of novel technology-based interventions for ischemic stroke.
Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
Feb 18, 2021
PURPOSE: To identify novel technologies pertinent to the prevention, diagnosis, treatment, and rehabilitation of ischemic stroke, and recommend the technologies that show the most promise in advancing ischemic stroke care.
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