AIMC Topic: Brain Ischemia
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Automated Segmentation of Intracranial Thrombus on NCCT and CTA in Patients with Acute Ischemic Stroke Using a Coarse-to-Fine Deep Learning Model.
AJNR. American journal of neuroradiology
May 18, 2023
BACKGROUND AND PURPOSE: Identifying the presence and extent of intracranial thrombi is crucial in selecting patients with acute ischemic stroke for treatment. This article aims to develop an automated approach to quantify thrombus on NCCT and CTA in ...
Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke.
Tomography (Ann Arbor, Mich.)
Mar 16, 2023
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...
DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy.
European radiology
Feb 27, 2023
OBJECTIVES: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a mode...
Deep learning-based personalised outcome prediction after acute ischaemic stroke.
Journal of neurology, neurosurgery, and psychiatry
Jan 17, 2023
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
Jan 12, 2023
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...
Automated estimation of ischemic core volume on noncontrast-enhanced CT via machine learning.
Interventional neuroradiology : journal of peritherapeutic neuroradiology, surgical procedures and related neurosciences
Dec 26, 2022
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
Dec 6, 2022
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 ...
Deep learning-based behavioral profiling of rodent stroke recovery.
BMC biology
Oct 15, 2022
BACKGROUND: Stroke research heavily relies on rodent behavior when assessing underlying disease mechanisms and treatment efficacy. Although functional motor recovery is considered the primary targeted outcome, tests in rodents are still poorly reprod...
Interpretable deep learning for the prognosis of long-term functional outcome post-stroke using acute diffusion weighted imaging.
Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism
Sep 28, 2022
Advances in deep learning can be applied to acute stroke imaging to build powerful and explainable prediction models that could supersede traditionally used biomarkers. We aimed to evaluate the performance and interpretability of a deep learning mode...
Image level detection of large vessel occlusion on 4D-CTA perfusion data using deep learning in acute stroke.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Sep 10, 2022
OBJECTIVES: Automated image-level detection of large vessel occlusions (LVO) could expedite patient triage for mechanical thrombectomy. A few studies have previously attempted LVO detection using artificial intelligence (AI) on CT angiography (CTA) i...
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