A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke
Ischemic strokes (IS) and transient ischemic attacks (TIA) account for approximately 80% of all strokes and are leading causes of death worldwide. Assessing the risk of recurrence or functional impairment in IS and TIA patients is essential to both acute phase treatment and secondary prevention. Cur...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-04-01
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| Series: | Advanced Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/aisy.202200240 |
| _version_ | 1797842543413559296 |
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| author | Jing Jing Ziyang Liu Hao Guan Wanlin Zhu Zhe Zhang Xia Meng Jian Cheng Yuesong Pan Yong Jiang Yilong Wang Haijun Niu Xingquan Zhao Wei Wen Jinxi Lin Wei Li Hao Li Perminder S. Sachdev Tao Liu Zixiao Li Dacheng Tao Yongjun Wang |
| author_facet | Jing Jing Ziyang Liu Hao Guan Wanlin Zhu Zhe Zhang Xia Meng Jian Cheng Yuesong Pan Yong Jiang Yilong Wang Haijun Niu Xingquan Zhao Wei Wen Jinxi Lin Wei Li Hao Li Perminder S. Sachdev Tao Liu Zixiao Li Dacheng Tao Yongjun Wang |
| author_sort | Jing Jing |
| collection | DOAJ |
| description | Ischemic strokes (IS) and transient ischemic attacks (TIA) account for approximately 80% of all strokes and are leading causes of death worldwide. Assessing the risk of recurrence or functional impairment in IS and TIA patients is essential to both acute phase treatment and secondary prevention. Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning‐based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep‐learning stroke recurrence risk score, SRR score). Then, Kaplan–Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed. Using 15 166 Third China National Stroke Registry (CNSR‐III) cases, the RPS's receiver operating characteristic curve (AUC) values of 0.850 for 14 day TIA recurrence prediction and 0.837 for 3 month IS disability prediction are used. Among patients deemed high risk by SRR score, 22.9% and 24.4% of individuals with TIA and IS respectively have stroke recurrence within 1 year, which are significantly higher than the rates in low‐risk individuals. Deep learning‐based RPS can outperform conventional risk scores and has the potential to assist accurate prognostication in stroke patients to optimize management. |
| first_indexed | 2024-04-09T16:49:38Z |
| format | Article |
| id | doaj.art-f0f9c17e9892456ea23481f04294c3c0 |
| institution | Directory Open Access Journal |
| issn | 2640-4567 |
| language | English |
| last_indexed | 2024-04-09T16:49:38Z |
| publishDate | 2023-04-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj.art-f0f9c17e9892456ea23481f04294c3c02023-04-22T02:52:33ZengWileyAdvanced Intelligent Systems2640-45672023-04-0154n/an/a10.1002/aisy.202200240A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic StrokeJing Jing0Ziyang Liu1Hao Guan2Wanlin Zhu3Zhe Zhang4Xia Meng5Jian Cheng6Yuesong Pan7Yong Jiang8Yilong Wang9Haijun Niu10Xingquan Zhao11Wei Wen12Jinxi Lin13Wei Li14Hao Li15Perminder S. Sachdev16Tao Liu17Zixiao Li18Dacheng Tao19Yongjun Wang20Department of Neurology Beijing Tiantan Hospital Capital Medical University Beijing 10070 ChinaBeijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing 100191 ChinaUBTech Sydney Artificial Intelligence Institute School of Computer Science FEIT University of Sydney Darlington NSW 2006 AustraliaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaSchool of Computer Science and Engineering Beihang University Beijing 100191 ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaDepartment of Neurology Beijing Tiantan Hospital Capital Medical University Beijing 10070 ChinaBeijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing 100191 ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaCentre for Healthy Brain Ageing (CHeBA) School of Psychiatry UNSW Sydney NSW 2052 AustraliaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaChina National Clinical Research Center for Neurological Diseases Beijing Tiantan Hospital Capital Medical University Beijng 100070 ChinaCentre for Healthy Brain Ageing (CHeBA) School of Psychiatry UNSW Sydney NSW 2052 AustraliaBeijing Advanced Innovation Center for Biomedical Engineering School of Biological Science and Medical Engineering Beihang University Beijing 100191 ChinaDepartment of Neurology Beijing Tiantan Hospital Capital Medical University Beijing 10070 ChinaJD Explore Academy at JD.com Beijing 101111 ChinaDepartment of Neurology Beijing Tiantan Hospital Capital Medical University Beijing 10070 ChinaIschemic strokes (IS) and transient ischemic attacks (TIA) account for approximately 80% of all strokes and are leading causes of death worldwide. Assessing the risk of recurrence or functional impairment in IS and TIA patients is essential to both acute phase treatment and secondary prevention. Current risk prediction systems that rely on clinical parameters alone without leveraging imaging data have only modest performance. Herein, a deep learning‐based risk prediction system (RPS) is developed to predict the probability of stroke recurrence or disability (i.e., deep‐learning stroke recurrence risk score, SRR score). Then, Kaplan–Meier analysis to evaluate the ability of SRR score to stratify patients at stroke recurrence risk is discussed. Using 15 166 Third China National Stroke Registry (CNSR‐III) cases, the RPS's receiver operating characteristic curve (AUC) values of 0.850 for 14 day TIA recurrence prediction and 0.837 for 3 month IS disability prediction are used. Among patients deemed high risk by SRR score, 22.9% and 24.4% of individuals with TIA and IS respectively have stroke recurrence within 1 year, which are significantly higher than the rates in low‐risk individuals. Deep learning‐based RPS can outperform conventional risk scores and has the potential to assist accurate prognostication in stroke patients to optimize management.https://doi.org/10.1002/aisy.202200240deep learningischemic strokeprognosis predictionrisk stratificationtransient ischemic attacks |
| spellingShingle | Jing Jing Ziyang Liu Hao Guan Wanlin Zhu Zhe Zhang Xia Meng Jian Cheng Yuesong Pan Yong Jiang Yilong Wang Haijun Niu Xingquan Zhao Wei Wen Jinxi Lin Wei Li Hao Li Perminder S. Sachdev Tao Liu Zixiao Li Dacheng Tao Yongjun Wang A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke Advanced Intelligent Systems deep learning ischemic stroke prognosis prediction risk stratification transient ischemic attacks |
| title | A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke |
| title_full | A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke |
| title_fullStr | A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke |
| title_full_unstemmed | A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke |
| title_short | A Deep Learning System to Predict Recurrence and Disability Outcomes in Patients with Transient Ischemic Attack or Ischemic Stroke |
| title_sort | deep learning system to predict recurrence and disability outcomes in patients with transient ischemic attack or ischemic stroke |
| topic | deep learning ischemic stroke prognosis prediction risk stratification transient ischemic attacks |
| url | https://doi.org/10.1002/aisy.202200240 |
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