Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke

You, Jia; Tsang, Arthur; Yu, Philip L. H.; Tsui, Eva L. H.; Woo, Pauline P. S.; Lui, Carrie Siu Man; Leung, Gilberto Ka-Kit

doi:10.3389/fninf.2020.00013

Cited by 22 publications

(19 citation statements)

References 35 publications

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“…They found that the ANN had a higher predictive value than the conventional scales (AUROC, ANN 0.823, conventional scales 0.740–0.796) 12 . In addition, prediction algorithms for LVOs using machine learning were documented by You et al, whose study was not a prehospital study but a hospital study 13 . Their study included 300 adult stroke patients (LVO n = 130) with 24 variables and compared the predictive values of XGBoost, logistic regression, random forest, and SVM.…”

Section: Discussionmentioning

confidence: 99%

“…Their study included 300 adult stroke patients (LVO n = 130) with 24 variables and compared the predictive values of XGBoost, logistic regression, random forest, and SVM. They found that XGBoost had the highest predictive value for LVOs (AUROC 0.809) 13 . In agreement with these findings, we also found that XGBoost had a higher predictive value for strokes compared to logistic regression, random forest, and SVM.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study

Hayashi

Shimada

Hattori

et al. 2021

Sci Rep

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High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational study in 12 hospitals in Japan. Five diagnostic algorithms using machine learning, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting, were evaluated for stroke and subcategories including acute ischemic stroke with/without large vessel occlusions, intracranial hemorrhage, and subarachnoid hemorrhage. Of the 1446 patients in the analysis, 1156 (80%) were randomly included in the training (derivation) cohort and cohorts, and 290 (20%) were included in the test (validation) cohort. In the diagnostic algorithms for strokes using eXtreme Gradient Boosting had the highest diagnostic value (test data, area under the receiver operating curve 0.980). In the diagnostic algorithms for the subcategories using eXtreme Gradient Boosting had a high predictive value (test data, area under the receiver operating curve, acute ischemic stroke with/without large vessel occlusions 0.898/0.882, intracranial hemorrhage 0.866, subarachnoid hemorrhage 0.926). Prehospital diagnostic algorithms using machine learning had high predictive value for strokes and their subcategories.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study

Hayashi

Shimada

Hattori

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…They found that the ANN had a higher predictive value than the conventional scales (AUROC, ANN 0.823, conventional scales 0.740-0.796) 12 . In addition, prediction algorithms for LVOs using machine learning were documented by You et al, whose study was not a prehospital study but a hospital study 13 .…”

Section: Prediction Of Stroke Subcategoriesmentioning

confidence: 99%

“…Their study included 300 adult stroke patients (LVO n = 130) with 24 variables and compared the predictive values of XGBoost, logistic regression, random forest, and SVM. They found that XGBoost had the highest predictive value for LVOs (AUROC 0.809) 13 . In agreement with these ndings, we also found that XGBoost had a higher predictive value for strokes compared to logistic regression, random forest, and SVM.…”

Section: Prediction Of Stroke Subcategoriesmentioning

confidence: 99%

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study

Hayashi

Shimada

Hattori

et al. 2021

Preprint

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High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions (LVOs). We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational study in 12 hospitals in Japan. Five diagnostic algorithms using machine learning, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost), were evaluated for stroke and subcategories including acute ischemic stroke (AIS) with/without LVO, intracranial hemorrhage (ICH), and subarachnoid hemorrhage (SAH). Of the 1446 patients in the analysis, 1156 (80%) were randomly included in the training cohort and 290 (20%) were included in the test cohort. In the diagnostic algorithms for strokes using XGBoost had the highest diagnostic value (test data, area under the receiver operating curve [AUROC] 0.980, confidence interval [CI; 0.962–0.994]). In the diagnostic algorithms for the subcategories using XGBoost had a high predictive value (test data, AUROC [CI], AIS with LVO 0.898 [0.848–0.939], AIS without LVO 0.882 [0.836–0.923], ICH 0.866 [0.817–0.911], SAH 0.926 [0.874–0.971]). Prehospital diagnostic algorithms using machine learning had high predictive value for strokes and their subcategories.

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“…Automatic segmentation of cerebral vessels is required to perform automatic analysis of CoW variation. However, it is still challenging to segment cerebral arteries automatically due to the inhomogeneous intensity, complex topological shapes, vessel abnormality, and other complex features [ 14 , 15 ]. Conventional methods segment vessels are based on low-level features with low accuracy and efficiency [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Relationship between Circle of Willis Variations and Cerebral or Cervical Arteries Stenosis Investigated by Computer Tomography Angiography and Multitask Convolutional Neural Network

Hou

Gao

Pan

et al. 2021

Journal of Healthcare Engineering

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Circle of Willis (CoW) is the most critical collateral pathway that supports the redistribution of blood supply in the brain. The variation of CoW is closely correlated with cerebral hemodynamic and cerebral vessel-related diseases. But what is responsible for CoW variation remains unclear. Moreover, the visual evaluation for CoW variation is highly time-consuming. In the present study, based on the computer tomography angiography (CTA) dataset from 255 patients, the correlation between the CoW variations with age, gender, and cerebral or cervical artery stenosis was investigated. A multitask convolutional neural network (CNN) was used to segment cerebral arteries automatically. The results showed the prevalence of variation of the anterior communicating artery (Aco) was higher in the normal senior group than in the normal young group and in females than in males. The changes in the prevalence of variations of individual segments were not demonstrated in the population with stenosis of the afferent and efferent arteries, so the critical factors for variation are related to genetic or physiological factors rather than pathological lesions. Using the multitask CNN model, complete cerebral and cervical arteries could be segmented and reconstructed in 120 seconds, and an average Dice coefficient of 78.2% was achieved. The segmentation accuracy for precommunicating part of anterior cerebral artery and posterior cerebral artery, the posterior communicating arteries, and Aco in CoW was 100%, 99.2%, 94%, and 69%, respectively. Artificial intelligence (AI) can be considered as an adjunct tool for detecting the CoW, particularly related to reducing workload and improving the accuracy of the visual evaluation. The study will serve as a basis for the following research to determine an individual’s risk of stroke with the aid of AI.

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Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke

Cited by 22 publications

References 35 publications

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study

Relationship between Circle of Willis Variations and Cerebral or Cervical Arteries Stenosis Investigated by Computer Tomography Angiography and Multitask Convolutional Neural Network

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