Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke
BackgroundThe detection of large vessel occlusion (LVO) plays a critical role in the diagnosis and treatment of acute ischemic stroke (AIS). Identifying LVO in the pre-hospital setting or early stage of hospitalization would increase the patients’ chance of receiving appropriate reperfusion therapy...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-03-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fninf.2020.00013/full |
| _version_ | 1818896474070581248 |
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| author | Jia You Anderson C. O. Tsang Philip L. H. Yu Eva L. H. Tsui Pauline P. S. Woo Carrie S. M. Lui Gilberto K. K. Leung |
| author_facet | Jia You Anderson C. O. Tsang Philip L. H. Yu Eva L. H. Tsui Pauline P. S. Woo Carrie S. M. Lui Gilberto K. K. Leung |
| author_sort | Jia You |
| collection | DOAJ |
| description | BackgroundThe detection of large vessel occlusion (LVO) plays a critical role in the diagnosis and treatment of acute ischemic stroke (AIS). Identifying LVO in the pre-hospital setting or early stage of hospitalization would increase the patients’ chance of receiving appropriate reperfusion therapy and thereby improve neurological recovery.MethodsTo enable rapid identification of LVO, we established an automated evaluation system based on all recorded AIS patients in Hong Kong Hospital Authority’s hospitals in 2016. The 300 study samples were randomly selected based on a disproportionate sampling plan within the integrated electronic health record system, and then separated into a group of 200 patients for model training, and another group of 100 patients for model performance evaluation. The evaluation system contained three hierarchical models based on patients’ demographic data, clinical data and non-contrast CT (NCCT) scans. The first two levels of modeling utilized structured demographic and clinical data, while the third level involved additional NCCT imaging features obtained from deep learning model. All three levels’ modeling adopted multiple machine learning techniques, including logistic regression, random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGboost). The optimal cut-off for the likelihood of LVO was determined by the maximal Youden index based on 10-fold cross-validation. Comparisons of performance on the testing group were made between these techniques.ResultsAmong the 300 patients, there were 160 women and 140 men aged from 27 to 104 years (mean 76.0 with standard deviation 13.4). LVO was present in 130 (43.3%) patients. Together with clinical and imaging features, the XGBoost model at the third level of evaluation achieved the best model performance on testing group. The Youden index, accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) were 0.638, 0.800, 0.953, 0.684, 0.804, and 0.847, respectively.ConclusionTo the best of our knowledge, this is the first study combining both structured clinical data with non-structured NCCT imaging data for the diagnosis of LVO in the acute setting, with superior performance compared to previously reported approaches. Our system is capable of automatically providing preliminary evaluations at different pre-hospital stages for potential AIS patients. |
| first_indexed | 2024-12-19T19:00:51Z |
| format | Article |
| id | doaj.art-bd0ba7dbac8541d6b5bca4cbb09594ba |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-12-19T19:00:51Z |
| publishDate | 2020-03-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-bd0ba7dbac8541d6b5bca4cbb09594ba2022-12-21T20:09:34ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962020-03-011410.3389/fninf.2020.00013516577Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia StrokeJia You0Anderson C. O. Tsang1Philip L. H. Yu2Eva L. H. Tsui3Pauline P. S. Woo4Carrie S. M. Lui5Gilberto K. K. Leung6Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong KongDivision of Neurosurgery, Department of Surgery, The University of Hong Kong, Hong Kong, Hong KongDepartment of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, Hong KongDepartment of Statistics and Workforce Planning, Hospital Authority, Hong Kong, Hong KongDepartment of Statistics and Workforce Planning, Hospital Authority, Hong Kong, Hong KongDepartment of Statistics and Workforce Planning, Hospital Authority, Hong Kong, Hong KongDivision of Neurosurgery, Department of Surgery, The University of Hong Kong, Hong Kong, Hong KongBackgroundThe detection of large vessel occlusion (LVO) plays a critical role in the diagnosis and treatment of acute ischemic stroke (AIS). Identifying LVO in the pre-hospital setting or early stage of hospitalization would increase the patients’ chance of receiving appropriate reperfusion therapy and thereby improve neurological recovery.MethodsTo enable rapid identification of LVO, we established an automated evaluation system based on all recorded AIS patients in Hong Kong Hospital Authority’s hospitals in 2016. The 300 study samples were randomly selected based on a disproportionate sampling plan within the integrated electronic health record system, and then separated into a group of 200 patients for model training, and another group of 100 patients for model performance evaluation. The evaluation system contained three hierarchical models based on patients’ demographic data, clinical data and non-contrast CT (NCCT) scans. The first two levels of modeling utilized structured demographic and clinical data, while the third level involved additional NCCT imaging features obtained from deep learning model. All three levels’ modeling adopted multiple machine learning techniques, including logistic regression, random forest, support vector machine (SVM), and eXtreme Gradient Boosting (XGboost). The optimal cut-off for the likelihood of LVO was determined by the maximal Youden index based on 10-fold cross-validation. Comparisons of performance on the testing group were made between these techniques.ResultsAmong the 300 patients, there were 160 women and 140 men aged from 27 to 104 years (mean 76.0 with standard deviation 13.4). LVO was present in 130 (43.3%) patients. Together with clinical and imaging features, the XGBoost model at the third level of evaluation achieved the best model performance on testing group. The Youden index, accuracy, sensitivity, specificity, F1 score, and area under the curve (AUC) were 0.638, 0.800, 0.953, 0.684, 0.804, and 0.847, respectively.ConclusionTo the best of our knowledge, this is the first study combining both structured clinical data with non-structured NCCT imaging data for the diagnosis of LVO in the acute setting, with superior performance compared to previously reported approaches. Our system is capable of automatically providing preliminary evaluations at different pre-hospital stages for potential AIS patients.https://www.frontiersin.org/article/10.3389/fninf.2020.00013/fullacute ischemic strokelarge vessel occlusionprognosismachine learningdeep learning |
| spellingShingle | Jia You Anderson C. O. Tsang Philip L. H. Yu Eva L. H. Tsui Pauline P. S. Woo Carrie S. M. Lui Gilberto K. K. Leung Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke Frontiers in Neuroinformatics acute ischemic stroke large vessel occlusion prognosis machine learning deep learning |
| title | Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke |
| title_full | Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke |
| title_fullStr | Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke |
| title_full_unstemmed | Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke |
| title_short | Automated Hierarchy Evaluation System of Large Vessel Occlusion in Acute Ischemia Stroke |
| title_sort | automated hierarchy evaluation system of large vessel occlusion in acute ischemia stroke |
| topic | acute ischemic stroke large vessel occlusion prognosis machine learning deep learning |
| url | https://www.frontiersin.org/article/10.3389/fninf.2020.00013/full |
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