Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke
Background: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to...
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MDPI AG
2023-03-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/9/2/52 |
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| author | Chun-Chao Huang Hsin-Fan Chiang Cheng-Chih Hsieh Chao-Liang Chou Zong-Yi Jhou Ting-Yi Hou Jin-Siang Shaw |
| author_facet | Chun-Chao Huang Hsin-Fan Chiang Cheng-Chih Hsieh Chao-Liang Chou Zong-Yi Jhou Ting-Yi Hou Jin-Siang Shaw |
| author_sort | Chun-Chao Huang |
| collection | DOAJ |
| description | Background: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to use an artificial intelligence (AI) technique to develop an automatic AI prediction model for the collateral status of mCTA. Methods: This retrospective study enrolled subjects with acute ischemic stroke receiving endovascular thrombectomy between January 2015 and June 2020 in a tertiary referral hospital. The demographic data and images of mCTA were collected. The collateral status of all mCTA was visually evaluated. Images at the basal ganglion and supraganglion levels of mCTA were selected to produce AI models using the convolutional neural network (CNN) technique to automatically predict the collateral status of mCTA. Results: A total of 82 subjects were enrolled. There were 57 cases randomly selected for the training group and 25 cases for the validation group. In the training group, there were 40 cases with a positive collateral result (good or intermediate) and 17 cases with a negative collateral result (poor). In the validation group, there were 21 cases with a positive collateral result and 4 cases with a negative collateral result. During training for the CNN prediction model, the accuracy of the training group could reach 0.999 ± 0.015, whereas the prediction model had a performance of 0.746 ± 0.008 accuracy on the validation group. The area under the ROC curve was 0.7. Conclusions: This study suggests that the application of the AI model derived from mCTA images to automatically evaluate the collateral status is feasible. |
| first_indexed | 2024-03-11T04:29:02Z |
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| language | English |
| last_indexed | 2024-03-11T04:29:02Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj.art-60aadd93a30c4202bc6c7b6d6c9347a32023-11-17T21:36:29ZengMDPI AGTomography2379-13812379-139X2023-03-019264765610.3390/tomography9020052Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic StrokeChun-Chao Huang0Hsin-Fan Chiang1Cheng-Chih Hsieh2Chao-Liang Chou3Zong-Yi Jhou4Ting-Yi Hou5Jin-Siang Shaw6Department of Radiology, MacKay Memorial Hospital, Taipei 104217, TaiwanDepartment of Radiology, MacKay Memorial Hospital, Taipei 104217, TaiwanDepartment of Radiology, MacKay Memorial Hospital, Taipei 104217, TaiwanDepartment of Medicine, MacKay Medical College, New Taipei City 252005, TaiwanInstitute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, TaiwanInstitute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, TaiwanInstitute of Mechatronic Engineering, National Taipei University of Technology, Taipei 106344, TaiwanBackground: Collateral status is an important predictor for the outcome of acute ischemic stroke with large vessel occlusion. Multiphase computed-tomography angiography (mCTA) is useful to evaluate the collateral status, but visual evaluation of this examination is time-consuming. This study aims to use an artificial intelligence (AI) technique to develop an automatic AI prediction model for the collateral status of mCTA. Methods: This retrospective study enrolled subjects with acute ischemic stroke receiving endovascular thrombectomy between January 2015 and June 2020 in a tertiary referral hospital. The demographic data and images of mCTA were collected. The collateral status of all mCTA was visually evaluated. Images at the basal ganglion and supraganglion levels of mCTA were selected to produce AI models using the convolutional neural network (CNN) technique to automatically predict the collateral status of mCTA. Results: A total of 82 subjects were enrolled. There were 57 cases randomly selected for the training group and 25 cases for the validation group. In the training group, there were 40 cases with a positive collateral result (good or intermediate) and 17 cases with a negative collateral result (poor). In the validation group, there were 21 cases with a positive collateral result and 4 cases with a negative collateral result. During training for the CNN prediction model, the accuracy of the training group could reach 0.999 ± 0.015, whereas the prediction model had a performance of 0.746 ± 0.008 accuracy on the validation group. The area under the ROC curve was 0.7. Conclusions: This study suggests that the application of the AI model derived from mCTA images to automatically evaluate the collateral status is feasible.https://www.mdpi.com/2379-139X/9/2/52multiphase CTAcollateral statusartificial intelligenceconvolutional neural networkacute ischemic stroke |
| spellingShingle | Chun-Chao Huang Hsin-Fan Chiang Cheng-Chih Hsieh Chao-Liang Chou Zong-Yi Jhou Ting-Yi Hou Jin-Siang Shaw Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke Tomography multiphase CTA collateral status artificial intelligence convolutional neural network acute ischemic stroke |
| title | Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke |
| title_full | Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke |
| title_fullStr | Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke |
| title_full_unstemmed | Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke |
| title_short | Using Deep-Learning-Based Artificial Intelligence Technique to Automatically Evaluate the Collateral Status of Multiphase CTA in Acute Ischemic Stroke |
| title_sort | using deep learning based artificial intelligence technique to automatically evaluate the collateral status of multiphase cta in acute ischemic stroke |
| topic | multiphase CTA collateral status artificial intelligence convolutional neural network acute ischemic stroke |
| url | https://www.mdpi.com/2379-139X/9/2/52 |
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