scholarly journals Аpplication of artificial intelligence systems in neuroradiology of acute ischemic stroke

2021 ◽  
Vol 12 (2) ◽  
pp. 30-35
Author(s):  
Р. L. Andropova ◽  
P. V. Gavrilov ◽  
Zh. I. Savintseva ◽  
А. V. Vovk ◽  
Е. V. Rybin
Keyword(s):  
Artificial Intelligence ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Disease Onset ◽  
Imaging Analysis ◽  
Current State ◽  
Automatic Image Processing ◽  
Neuroimaging Data ◽  
First Results ◽  
Lesion Analysis

Introduction. Artificial intelligence is one of the fastest-growing areas of great importance to radiology. Purpose. In this article, we aimed to study the current state of the use of computer-aided imaging analysis in acute ischemic stroke. Results. There are many artificial intelligence softwares that automatic image processing can successfully identify neuroradiology image in stroke: early detection by diagnostic imaging methods, assessment of the time of disease onset, segmentation of the lesion, analysis of the presence and possibility of cerebral edema, and predicting complications and treatment outcomes. Conclusion. The first results of using artificial intelligence to evaluate neuroimaging data showed that machine-learning methods could be useful as decision-making tools when choosing a treatment for acute ischemic stroke.

scholarly journals Neuroimaging of Acute Ischemic Stroke: Current State

2019 ◽  
Vol 6 (2) ◽  
pp. 12-17
Author(s):  
R. Kh. Aldatov ◽  
G. E. Trufanov ◽  
V. A. Fokin
Keyword(s):  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Current State

Artificial Intelligence in Acute Ischemic Stroke

2021 ◽  
pp. 1-17
Author(s):  
Freda Werdiger ◽  
Andrew Bivard ◽  
Mark Parsons
Keyword(s):  
Artificial Intelligence ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke

ASPECTS discrepancies between CT and MR imaging: analysis and implications for triage protocols in acute ischemic stroke

2016 ◽  
Vol 9 (3) ◽  
pp. 240-243 ◽  
Author(s):  
Ferdinand K Hui ◽  
Nancy A Obuchowski ◽  
Seby John ◽  
Gabor Toth ◽  
Irene Katzan ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Diffusion Weighted Imaging ◽  
Multivariate Analyses ◽  
National Institutes Of Health ◽  
Imaging Analysis ◽  
Multivariate Logistic Regression ◽  
The Cost ◽  
Optimal Imaging

BackgroundOptimal imaging triage for intervention for large vessel occlusions remains unclear. MR-based imaging provides ischemic core volumes at the cost of increased imaging time. CT Alberta Stroke Program Early CT Score (ASPECTS) estimates are faster, but may be less sensitive.ObjectiveTo assesses the rate at which MRI changed management in comparison with CT imaging alone.MethodsRetrospective analysis of patients with acute ischemic stroke undergoing imaging triage for endovascular therapy was performed between 2008 and 2013. Univariate and multivariate analyses were performed. Multivariate logistic regression was used to evaluate the effect of time on disagreement in MRI and CT ASPECTS scores.ResultsA total of 241 patients underwent both diffusion-weighted imaging (DWI) and CT. Six patients with DWI ASPECTS ≥6 and CT ASPECTS <6 were omitted, leaving 235 patients. For 47 patients, disagreement between the two modalities resulted in different treatment recommendations. The estimated probability of disagreement was 20.0% (95% CI 15.4% to 25.6%). In a multivariate logistic regression, CT ASPECTS >7 (p=0.004) and admission National Institutes of Health Stroke Scale (NIHSS) score <16 (p=0.008) were simultaneously significant predictors of agreement in ASPECTS. The time between modalities was a marginally significant predictor (p=0.080).ConclusionsThe study suggests that patients with NIHSS scores at admission of <16 and patients with CT ASPECTS >7 have a higher likelihood of agreement between CT and DWI based on an ASPECTS cut-off value of 6. Additional MRI for triage in patients with NIHSS at admission of >16, and ASPECTS of 6 or 7 may be more likely to change management. Unsurprisingly, patients with low CT ASPECTS had good correlation with MRI ASPECTS.

Validation of an artificial intelligence-driven large vessel occlusion detection algorithm for acute ischemic stroke patients

2021 ◽  
pp. 197140092199895
Author(s):  
Ryan A Rava ◽  
Blake A Peterson ◽  
Samantha E Seymour ◽  
Kenneth V Snyder ◽  
Maxim Mokin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Ischemic Stroke ◽  
Acute Ischemic Stroke ◽  
Correlation Coefficients ◽  
Detection Algorithm ◽  
Large Vessel ◽  
Occlusion Detection ◽  
Clinical Workflow ◽  
Vessel Occlusion ◽  
Clinical Scenarios

Rapid and accurate diagnosis of large vessel occlusions (LVOs) in acute ischemic stroke (AIS) patients using automated software could improve clinical workflow in determining thrombectomy in eligible patients. Artificial intelligence-based methods could accomplish this; however, their performance in various clinical scenarios, relative to clinical experts, must be thoroughly investigated. We aimed to assess the ability of Canon’s AUTOStroke Solution LVO application in properly detecting and locating LVOs in AIS patients. Data from 202 LVO and 101 non-LVO AIS patients who presented with stroke-like symptoms between March 2019 and February 2020 were collected retrospectively. LVO patients had either an internal carotid artery (ICA) ( n = 59), M1 middle cerebral artery (MCA) ( n = 82) or M2 MCA ( n = 61) occlusion. Computed tomography angiography (CTA) scans from each patient were pushed to the automation platform and analyzed. The algorithm’s ability to detect LVOs was assessed using accuracy, sensitivity and Matthews correlation coefficients (MCCs) for each occlusion type. The following results were calculated for each occlusion type in the study (accuracy, sensitivity, MCC): ICA = (0.95, 0.90, 0.89), M1 MCA = (0.89, 0.77, 0.78) and M2 MCA = (0.80, 0.51, 0.59). For the non-LVO cohort, 98% (99/101) of cases were correctly predicted as LVO negative. Processing time for each case was 69.8 ± 1.1 seconds (95% confidence interval). Canon’s AUTOStroke Solution LVO application was able to accurately identify ICA and M1 MCA occlusions in addition to almost perfectly assessing when an LVO was not present. M2 MCA occlusion detection needs further improvement based on the sensitivity results displayed by the LVO detection algorithm.

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