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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

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

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

Detection of early infarction signs with machine learning-based diagnosis by means of the Alberta Stroke Program Early CT score (ASPECTS) in the clinical routine
ConclusionFor ASPECTS assessment, the examined software may provide valid data in case of normal brain. It may enhance the work of neuroradiologists in clinical decision making. A final human check for plausibility is needed, particularly in patient groups with pre-existing cerebral changes.
Source: Neuroradiology - July 31, 2018 Category: Radiology Source Type: research

Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke
ConclusionThe deep learning method appears potentially beneficial for identifying HMCAS on non-contrast CT in patients with acute ischemic stroke.
Source: Japanese Journal of Radiology - October 30, 2019 Category: Radiology Source Type: research

A clinical –radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study
ConclusionThe proposed clinical –radiomics model is a dependable approach that could provide risk assessment of HT for patients who receive IVT after stroke.
Source: Insights into Imaging - March 29, 2023 Category: Radiology Source Type: research

Can Shape Analysis Differentiate Free-floating Internal Carotid Artery Thrombus from Atherosclerotic Plaque in Patients Evaluated with CTA for Stroke or Transient Ischemic Attack?
Conclusions: We identified five quantitative shape descriptors of carotid FFT. This shape “signature” shows potential for supplementing conventional lesion characterization in cases of suspected FFT.
Source: Academic Radiology - February 8, 2014 Category: Radiology Authors: Rebecca E. Thornhill, Cheemun Lum, Arash Jaberi, Pawel Stefanski, Carlos H. Torres, Franco Momoli, William Petrcich, Dar Dowlatshahi Tags: Original Investigations Source Type: research

Deep Learning in the Prediction of Ischaemic Stroke Thrombolysis Functional Outcomes: A Pilot Study
ConclusionDL models may aid in the prediction of functional thrombolysis outcomes. Further investigation with larger datasets and additional imaging sequences is indicated.
Source: Academic Radiology - May 2, 2019 Category: Radiology Source Type: research

Abstract No. 720 Identification of irreversibly damaged brain tissue on computed tomography perfusion using convolutional neural network to assist selection for mechanical thrombectomy in ischemic stroke patients
Endovascular treatment of ischemic stroke has shown positive clinical outcomes. Further optimization requires identifying patients who will benefit from reperfusion. We propose using deep learning, specifically 3D convolutional neural networks (CNN), to identify infarcted tissue (core) on CT perfusion (CTP) with diffusion weighted imaging (DWI) MRI as gold standard for irreversible brain infarction and evaluate lesion size impact on the network ’s performance.
Source: Journal of Vascular and Interventional Radiology : JVIR - February 20, 2020 Category: Radiology Authors: R. Wang, K. Chang, H. Zhou, J. Wu, G. Cohan, M. Jayaraman, R. Huang, J. Boxerman, L. Yang, F. Hui, J. Woo, H. Bai Tags: Scientific Traditional Poster Source Type: research

Developing new quantitative CT image markers to predict prognosis of acute ischemic stroke patients
CONCLUSIONS: This study demonstrates feasibility of developing a new quantitative imaging method and marker to predict AIS patients' prognosis in the hyperacute stage, which can help clinicians optimally treat and manage AIS patients.PMID:35213340 | DOI:10.3233/XST-221138
Source: Journal of X-Ray Science and Technology - February 25, 2022 Category: Radiology Authors: Gopichandh Danala Bappaditya Ray Masoom Desai Morteza Heidari Seyedehnafiseh Mirniaharikandehei Sai Kiran R Maryada Bin Zheng Source Type: research

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