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Total 224 results found since Jan 2013.

Integrating New Staff into Endovascular Stroke-Treatment Workflows in the COVID-19 Pandemic INTERVENTIONAL
SUMMARY: A health care crisis such as the coronavirus disease 2019 (COVID-19) pandemic requires allocation of hospital staff and resources on short notice. Thus, new and sometimes less experienced team members might join the team to fill in the gaps. This scenario can be particularly challenging in endovascular stroke treatment, which is a highly specialized task that requires seamless cooperation of numerous health care workers across various specialties and professions. This document is intended for stroke teams who face the challenge of integrating new team members into endovascular stroke-treatment workflows during the...
Source: American Journal of Neuroradiology - January 11, 2021 Category: Radiology Authors: Goyal, M., Kromm, J., Ganesh, A., Wira, C., Southerland, A., Sheth, K. N., Khosravani, H., Panagos, P., McNair, N., Ospel, J. M., On behalf of the AHA/ASA Stroke Council Science Subcommittees: Emergency Neurovascular Care (ENCC), the Cardiovascular and St Tags: INTERVENTIONAL Source Type: research

Reduced striatal activation in response to rewarding motor performance feedback after stroke
ConclusionStriatal hypoactivation in stroke survivors may cause impaired consolidation of motor skills. Stronger rewarding stimuli or drug-mediated enhancement may be needed to normalize reward processing after stroke with positive effects on recovery.
Source: NeuroImage: Clinical - October 24, 2019 Category: Radiology Source Type: research

Rapid Assessment of Acute Ischemic Stroke by Computed Tomography Using Deep Convolutional Neural Networks
This study proposes an automatic identification scheme for acute ischemic stroke using deep convolutional neural networks (DCNNs) based on non-contrast computed tomographic (NCCT) images. Our image database for the classification model was composed of 1254 grayscale NCCT images from 96 patients (573 images) with acute ischemic stroke and 121 normal controls (681 images). According to the consensus of critical stroke findings by two neuroradiologists, a gold standard was established and used to train the proposed DCNN using machine-generated image features. Including the earliest DCNN, AlexNet, the popular Inception-v3, and...
Source: Journal of Digital Imaging - May 7, 2021 Category: Radiology Source Type: research

Development and external validation of a stability machine learning model to identify wake-up stroke onset time from MRI
ConclusionsThe svmRadial model using DWI  + FLAIR is the most stable and generalizable for identifying the onset time of wake-up stroke patients within 4.5 h of symptom onset.Key Points• Machining learning model helps clinicians to identify wake-up stroke patients within 4.5 h of symptom onset.• A prospective study showed that svmRadial model based on DWI + FLAIR was the most stable in predicting the stroke onset time.• External validation showed that svmRadial model has good generalization ability in predicting the stroke onset time.
Source: European Radiology - January 17, 2022 Category: Radiology Source Type: research

CT matches MRI for late-window stroke evaluation
Stroke patients who underwent endovascular therapy had similar improvement...Read more on AuntMinnie.comRelated Reading: Machine learning can predict stroke treatment outcomes Study reveals steep cost of delaying stroke treatment 3 CTA signs show which stroke patients can skip surgery Perfusion imaging expands window for stroke treatment CTA helps direct use of clot removal for stroke patients
Source: AuntMinnie.com Headlines - February 6, 2019 Category: Radiology Source Type: news

A Hybrid Approach for Sub-Acute Ischemic Stroke Lesion Segmentation Using Random Decision Forest and Gravitational Search Algorithm.
CONCLUSION: This paper provides a new hybrid GSA-RDF classifier technique to segment the ischemic stroke lesions in MR images. The experimental results demonstrate that the proposed technique has the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Bias Error (MBE) ranges are 16.5485 %, 7.2654 %, and 2.4585 %individually. The proposed RDF-GSA algorithm has better precision and execution when compared with the existing ischemic stroke segmentation method. PMID: 31975663 [PubMed - in process]
Source: Current Medical Imaging Reviews - January 26, 2020 Category: Radiology Tags: Curr Med Imaging Rev Source Type: research

Prediction of Clinical Outcome in Patients with Large-Vessel Acute Ischemic Stroke: Performance of Machine Learning versus SPAN-100 FUNCTIONAL
CONCLUSIONS: Machine learning–based feature selection can identify parameters with higher performance in outcome prediction. Machine learning models with the best-performing features, especially advanced CTP data, had superior performance of the recovery outcome prediction for patients with stroke at admission in comparison with SPAN-100.
Source: American Journal of Neuroradiology - February 9, 2021 Category: Radiology Authors: Jiang, B., Zhu, G., Xie, Y., Heit, J. J., Chen, H., Li, Y., Ding, V., Eskandari, A., Michel, P., Zaharchuk, G., Wintermark, M. Tags: FUNCTIONAL Source Type: research

Clot-based radiomics model for cardioembolic stroke prediction with CT imaging before recanalization: a multicenter study
ConclusionThe proposed CT-based radiomics model could reliably predict CE stroke in AIS, performing better than the routine radiological method.Key Points• Admission CT imaging could offer valuable information to identify the acute ischemic stroke source by radiomics analysis.• The proposed CT imaging–based radiomics model yielded a higher area under the curve (0.838) than the routine radiological method (0.713; p = 0.007).• Several radiomic features showed significantly stronger correlations with two main thrombus constituents (red blood cells, |rmax|, 0.74; fibrin and platelet, |rmax|, 0.68) than routine radiolog...
Source: European Radiology - September 6, 2022 Category: Radiology Source Type: research

Association between thrombus composition and stroke etiology in the MR CLEAN Registry biobank
ConclusionThrombus composition is significantly associated with stroke etiology, with an increase in RBC and a decrease in F/P raising the odds for a non-cardioembolic cause. No difference between composition of cardioembolic thrombi and of undetermined origin was seen. This emphasizes the need for more extensive monitoring for arrhythmias and/or extended cardiac analysis in case of an undetermined origin.
Source: Neuroradiology - April 15, 2023 Category: Radiology Source Type: research

SIR: Stroke treatment training program improves outcomes
Interventional radiologists at Johns Hopkins University have developed an innovative...Read more on AuntMinnie.comRelated Reading: MRI measurements of iron content show impact of stroke CT matches MRI for late-window stroke evaluation Machine learning can predict stroke treatment outcomes Study reveals steep cost of delaying stroke treatment MRI links lifestyle factors to stroke, dementia risk
Source: AuntMinnie.com Headlines - March 25, 2019 Category: Radiology Source Type: news

Improving Ischemic Stroke Care With MRI and Deep Learning Artificial Intelligence
Advanced magnetic resonance imaging has been used as selection criteria for both acute ischemic stroke treatment and secondary prevention. The use of artificial intelligence, and in particular, deep learning, to synthesize large amounts of data and to understand better how clinical and imaging data can be leveraged to improve stroke care promises a new era of stroke care. In this article, we review common deep learning model structures for stroke imaging, evaluation metrics for model performance, and studies that investigated deep learning application in acute ischemic stroke care and secondary prevention.
Source: Topics in Magnetic Resonance Imaging - August 1, 2021 Category: Radiology Tags: Review Articles Source Type: research

Accuracy and Prognostic Role of NCCT-ASPECTS Depend on Time from Acute Stroke Symptom-onset for both Human and Machine-learning Based Evaluation
ConclusionThe accuracy and reliability of NCCT-ASPECTS are time dependent for both human and machine-learning based evaluation, indicating reduced detectability of fast stroke progressors by NCCT. In hyperacute stroke, collateral status from CT-angiography may help for a  better prognosis on clinical outcome and explain the occurrence of futile recanalization.
Source: Klinische Neuroradiologie - October 28, 2021 Category: Radiology 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

Automated ASPECTS on Noncontrast CT Scans in Patients with Acute Ischemic Stroke Using Machine Learning FUNCTIONAL
CONCLUSIONS: The proposed automated ASPECTS scoring approach shows reasonable ability to determine ASPECTS on NCCT images in patients presenting with acute ischemic stroke.
Source: American Journal of Neuroradiology - January 11, 2019 Category: Radiology Authors: Kuang, H., Najm, M., Chakraborty, D., Maraj, N., Sohn, S. I., Goyal, M., Hill, M. D., Demchuk, A. M., Menon, B. K., Qiu, W. Tags: FUNCTIONAL Source Type: research