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Ischemic Stroke

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Ultrafast MRI using deep learning echoplanar imaging for a comprehensive assessment of acute ischemic stroke.

European radiology
OBJECTIVES: Acute ischemic stroke (AIS) is an emergency requiring both fast and informative MR sequences. We aimed to assess the performance of an artificial intelligence-enhanced ultrafast (UF) protocol, compared to the reference protocol, in the AI...

Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke.

Tomography (Ann Arbor, Mich.)
BACKGROUND: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this...

Machine learning-based approach reveals essential features for simplified TSPO PET quantification in ischemic stroke patients.

Zeitschrift fur medizinische Physik
INTRODUCTION: Neuroinflammation evaluation after acute ischemic stroke is a promising option for selecting an appropriate post-stroke treatment strategy. To assess neuroinflammation in vivo, translocator protein PET (TSPO PET) can be used. However, t...

Deep learning-based personalised outcome prediction after acute ischaemic stroke.

Journal of neurology, neurosurgery, and psychiatry
BACKGROUND: Whether deep learning models using clinical data and brain imaging can predict the long-term risk of major adverse cerebro/cardiovascular events (MACE) after acute ischaemic stroke (AIS) at the individual level has not yet been studied.

Deep learning for collateral evaluation in ischemic stroke with imbalanced data.

International journal of computer assisted radiology and surgery
PURPOSE: Collateral evaluation is typically done using visual inspection of cerebral images and thus suffers from intra- and inter-rater variability. Large open databases of ischemic stroke patients are rare, limiting the use of deep learning methods...

Lesion-preserving unpaired image-to-image translation between MRI and CT from ischemic stroke patients.

International journal of computer assisted radiology and surgery
PURPOSE: Multiple medical imaging modalities are used for clinical follow-up ischemic stroke analysis. Mixed-modality datasets are challenging, both for clinical rating purposes and for training machine learning models. While image-to-image translati...

Assessing electrocardiogram changes after ischemic stroke with artificial intelligence.

PloS one
OBJECTIVE: Ischemic stroke (IS) with subsequent cerebrocardiac syndrome (CCS) has a poor prognosis. We aimed to investigate electrocardiogram (ECG) changes after IS with artificial intelligence (AI).

Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning.

Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
BACKGROUND: Accurate estimation of ischemic core on baseline imaging has treatment implications in patients with acute ischemic stroke (AIS). Machine learning (ML) algorithms have shown promising results in estimating ischemic core using routine nonc...

Predicting Hypoperfusion Lesion and Target Mismatch in Stroke from Diffusion-weighted MRI Using Deep Learning.

Radiology
Background Perfusion imaging is important to identify a target mismatch in stroke but requires contrast agents and postprocessing software. Purpose To use a deep learning model to predict the hypoperfusion lesion in stroke and identify patients with ...

Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography.

PloS one
A non-contrast cranial computer tomography (ncCT) is often employed for the diagnosis of the early stage of the ischemic stroke. However, the number of false negatives is high. More accurate results are obtained by an MRI. However, the MRI is not ava...