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Automated prediction of final infarct volume in patients with large-vessel occlusion acute ischemic stroke
CONCLUSIONS: In a cohort of patients with LVO AIS in whom reperfusion was achieved, determinations of infarct core at presentation by NCHCT-ASPECTS and a machine learning model analyzing CTA source images were equivalent to CTP in predicting FIV. These findings have suggested that the information to accurately predict infarct core in patients with LVO AIS was present in conventional imaging modalities (NCHCT and CTA) and accessible by machine learning methods.PMID:34198252 | DOI:10.3171/2021.4.FOCUS21134
Source: Neurosurgical Focus - July 1, 2021 Category: Neurosurgery Authors: Rania Abdelkhaleq Youngran Kim Swapnil Khose Peter Kan Sergio Salazar-Marioni Luca Giancardo Sunil A Sheth Source Type: research

Machine learning based outcome prediction of large vessel occlusion of the anterior circulation prior to thrombectomy in patients with wake-up stroke
CONCLUSION: Machine learning algorithms have the potential to aid in the decision making for thrombectomy in patients with wake-up stroke especially in hospitals, where emergency CTP or MRI imaging is not available.PMID:36344011 | DOI:10.1177/15910199221135695
Source: Interventional Neuroradiology - November 7, 2022 Category: Radiology Authors: Ludger Feyen Christian Blockhaus Marcus Katoh Patrick Haage Christina Schaub Stefan Rohde Source Type: research

Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features.
Authors: Ho KC, Speier W, El-Saden S, Arnold CW Abstract Models have been developed to predict stroke outcomes (e.g., mortality) in attempt to provide better guidance for stroke treatment. However, there is little work in developing classification models for the problem of unknown time-since-stroke (TSS), which determines a patient's treatment eligibility based on a clinical defined cutoff time point (i.e., <4.5hrs). In this paper, we construct and compare machine learning methods to classify TSS<4.5hrs using magnetic resonance (MR) imaging features. We also propose a deep learning model to extract hidden rep...
Source: AMIA Annual Symposium Proceedings - March 20, 2019 Category: Bioinformatics Tags: AMIA Annu Symp Proc Source Type: research

A Machine Learning Approach for Classifying Ischemic Stroke Onset Time From Imaging
Current clinical practice relies on clinical history to determine the time since stroke (TSS) onset. Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options, such as thrombolysis. The patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this paper, we demonstrate a machine learning approach for TSS classification using routinely acquired imaging sequences. We develop imaging features from the magnetic resonance (MR) images and train machine learning ...
Source: IEE Transactions on Medical Imaging - June 30, 2019 Category: Biomedical Engineering Source Type: research

CT radiomics unlocks basal ganglia stroke onset time
The combination of radiomics and a machine-learning algorithm can determine...Read more on AuntMinnie.comRelated Reading: AI may help improve management of stroke patients AI finds infarction in stroke patients on unenhanced CT CT plus CT perfusion predicts stroke surgery outcomes CTA lowers costs, improves outcomes for minor stroke Can AI find brain hemorrhage as well as radiologists?
Source: AuntMinnie.com Headlines - February 11, 2020 Category: Radiology Source Type: news

Update on Treatment of Acute Ischemic Stroke
This article provides an update on the state of the art of the treatment of acute ischemic stroke with particular emphasis on the indications for reperfusion therapy. RECENT FINDINGS In addition to the previously established indications for intravenous (IV) thrombolysis with recombinant tissue plasminogen activator (rtPA) within 4.5 hours of stroke symptom onset and endovascular therapy with mechanical thrombectomy for patients with large artery occlusion who can be treated within 6 hours of symptom onset, recent randomized controlled trials have now established new indications for emergency reperfusion in patients with ...
Source: CONTINUUM: Lifelong Learning in Neurology - April 1, 2020 Category: Neurology Tags: REVIEW ARTICLES Source Type: research

The three-phase enriched environment paradigm promotes neurovascular restorative and prevents learning impairment after ischemic stroke in rats.
Abstract Enriched environment (EE) with a complex combination of sensorimotor, cognitive and social stimulations has been shown to enhance brain plasticity and improve recovery of functions in animal models of stroke. The present study extended these findings by assessing whether the three-phase EE intervention paradigm would improve neurovascular remodeling following ischemic stroke. Male Sprague-Dawley rats were subjected to permanent middle cerebral artery occlusion (MCAO). A three-phase EE intervention paradigm was designed in terms of the different periods of cerebral ischemia by periodically rearranging the ...
Source: Neurobiology of Disease - September 22, 2020 Category: Neurology Authors: Zhan Y, Li MZ, Yang L, Feng XF, Lei JF, Zhang N, Zhao YY, Zhao H Tags: Neurobiol Dis Source Type: research

Machine Learning-Based Prediction of Brain Tissue Infarction in Patients With Acute Ischemic Stroke Treated With Theophylline as an Add-On to Thrombolytic Therapy: A Randomized Clinical Trial Subgroup Analysis
Conclusions: The predicted follow-up brain lesions for each patient were not significantly different for patients virtually treated with theophylline or placebo, as an add-on to thrombolytic therapy. Thus, this study confirmed the lack of neuroprotective effect of theophylline shown in the main clinical trial and is contrary to the results from preclinical stroke models.
Source: Frontiers in Neurology - May 21, 2021 Category: Neurology Source Type: research

Ctbrain machine learning predicts stroke thrombolysis result
Conclusions This proof-of-concept study shows that machine learning methods applied to acute stroke CT-scans potentially offers automation, and improved performance in SICH prediction following thrombolysis. Larger-scale cohorts, and incorporation of CT perfusion/angiography data, should be tested with such methods.
Source: Journal of Neurology, Neurosurgery and Psychiatry - September 9, 2014 Category: Neurosurgery Authors: Epton, S., Bentley, P., Ganesalingam, J., Dias, A., Mahady, K., Rinne, P., Sharma, P., Halse, O., Mehta, A., Rueckert, D. Tags: Abstracts Source Type: research

Defining reperfusion post endovascular therapy in ischemic stroke using MR-dynamic contrast enhanced perfusion.
CONCLUSION: MR perfusion following EVT provides accurate physiological approach to understanding the relationship of CBF, clinical outcome, and DWI growth. ADVANCES IN KNOWLEDGE: MR perfusion CBF acquired is a robust, objective reperfusion measurement providing following recanalization of the target occlusion which is critical to distinguish potential therapeutic harm from the failed technical success of EVT as well as improve the responsiveness of clinical trial outcomes to disease modification. PMID: 32941770 [PubMed - as supplied by publisher]
Source: The British Journal of Radiology - September 16, 2020 Category: Radiology Authors: d'Esterre CD, Sah RG, Assis Z, Talai AS, Demchuk AM, Hill MD, Goyal M, Lee TY, Forkert ND, Barber PA Tags: Br J Radiol Source Type: research

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