Research progress of diagnosis and prediction system of stroke based on artificial intelligence_West China Medical Journal

Authors：

YE Zhenli ^1,2 , JIANG Jiaxin ³ ,  QU Yun ^1,2

1. Department of Rehabilitation Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
2. Key Laboratory of Rehabilitation Medicine in Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;
3. Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P. R. China;

Corresponding author：

QU Yun, Email: quyben@163.com

Keywords：

Stroke; artificial intelligence; prediction

DOI：

10.7507/1002-0179.202012292

Video：

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As a kind of disease with high incidence rate, high mortality, high recurrence rate and high disability rate, stroke has become one of the most serious disease burdens in China. Rapid diagnosis and treatment of stroke can effectively improve the outcome of patients and reduce the psychological and economic burden of patients’ families and society. In recent years, with the rapid development of artificial intelligence technology,this technology can effectively improve daily diagnosis and treatment efficiency. This paper focuses on the application of artificial intelligence technology to the diagnosis, treatment and outcome prediction of stroke, aiming to provide ideas for further guiding precision medicine.

Citation： YE Zhenli, JIANG Jiaxin, QU Yun. Research progress of diagnosis and prediction system of stroke based on artificial intelligence. West China Medical Journal, 2022, 37(12): 1887-1890. doi: 10.7507/1002-0179.202012292 Copy

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13.	丁玲玲, 李子孝, 王拥军. 人工智能临床决策支持系统在脑血管病中的应用. 中国卒中杂志, 2020, 15(3): 290-295.
14.	Weir CJ, Lees KR, MacWalter RS, et al. Cluster-randomized, controlled trial of computer-based decision support for selecting long-term anti-thrombotic therapy after acute ischaemic stroke. QJM, 2003, 96(2): 143-153.
15.	娄昕, 何建风, 马林. 脑血管病医学影像人工智能研究现状与展望. 中华放射学杂志, 2020, 54(5): 389-392.
16.	Li X, Wu M, Sun C, et al. Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients. Eur J Neurol, 2020, 27(8): 1656-1663.
17.	De Ryck A, Brouns R, Geurden M, et al. Risk factors for poststroke depression: identification of inconsistencies based on a systematic review. J Geriatr Psychiatry Neurol, 2014, 27(3): 147-158.
18.	Schepers V, Post M, Visser-Meily A, et al. Prediction of depressive symptoms up to three years post-stroke. J Rehabil Med, 2009, 41(11): 930-935.
19.	Liu R, Yue Y, Jiang H, et al. A risk prediction model for post-stroke depression in Chinese stroke survivors based on clinical and socio-psychological features. Oncotarget, 2017, 8(38): 62891-62899.
20.	Tung F, Mori G. Deep neural network compression by in-parallel pruning-quantization. IEEE Trans Pattern Anal Mach Intell, 2020, 42(3): 568-579.
21.	Huang G, Liu Z, Pleiss G, et al. Convolutional networks with dense connectivity. IEEE Trans Pattern Anal Mach Intell, 2022, 44(12): 8704-8716.
22.	Heo J, Yoon JG, Park H, et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke, 2019, 50(5): 1263-1265.
23.	Hilbert A, Ramos LA, van Os HJA, et al. Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke. Comput Biol Med, 2019, 115: 103516.
24.	Cheon S, Kim J, Lim J. The use of deep learning to predict stroke patient mortality. Int J Environ Res Public Health, 2019, 16(11): 1876.
25.	Lin WY, Chen CH, Tseng YJ, et al. Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation. Int J Med Inform, 2018, 111: 159-164.
26.	Nielsen A, Hansen MB, Tietze A, et al. Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke, 2018, 49(6): 1394-1401.
27.	Tekkeşin Aİ. Artificial intelligence in healthcare: past, present and future. Anatol J Cardiol, 2019, 22(Suppl 2): 8-9.

1. Feng R, Badgeley M, Mocco J, et al. Deep learning guided stroke management: a review of clinical applications. J Neurointerv Surg, 2018, 10(4): 358-362.
2. Hassabis D, Kumaran D, Summerfield C, et al. Neuroscience-inspired artificial intelligence. Neuron, 2017, 95(2): 245-258.
3. 严舒, 陈娟, 欧阳昭连. 基于 NIH 项目的美国医学人工智能发展态势分析. 中国医疗设备, 2019, 34(12): 101-105, 150.
4. 黄光成, 周良, 石建伟, 等. 机器学习算法在疾病风险预测中的应用与比较. 中国卫生资源, 2020, 23(4): 432-436.
5. Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw, 2015, 61: 85-117.
6. Dhakan P, Merrick K, Rañó I, et al. Intrinsic rewards for maintenance, approach, avoidance, and achievement goal types. Front Neurorobot, 2018, 12: 63.
7. 王拥军, 李子孝, 谷鸿秋, 等. 中国卒中报告 2020(中文版)(1). 中国卒中杂志, 2022, 17(5): 433-447.
8. Chilamkurthy S, Ghosh R, Tanamala S, et al. Deep learning algorithms for detection of critical findings in head CT scans: a retrospective study. Lancet, 2018, 392(10162): 2388-2396.
9. Titano JJ, Badgeley M, Schefflein J, et al. Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nat Med, 2018, 24(9): 1337-1341.
10. Claassen J, Doyle K, Matory A, et al. Detection of brain activation in unresponsive patients with acute brain injury. N Engl J Med, 2019, 380(26): 2497-2505.
11. 王霁雯, 林雨, 熊建华, 等. 基于深度学习的自发性脑出血 CT 影像分割算法精准计算病灶体积的应用探讨. 中华放射学杂志, 2019, 53(11): 941-945.
12. 陆言巧, 沈兰, 何奔. 人工智能在心血管疾病的辅助诊疗中的应用. 上海交通大学学报(医学版), 2020, 40(2): 259-262.
13. 丁玲玲, 李子孝, 王拥军. 人工智能临床决策支持系统在脑血管病中的应用. 中国卒中杂志, 2020, 15(3): 290-295.
14. Weir CJ, Lees KR, MacWalter RS, et al. Cluster-randomized, controlled trial of computer-based decision support for selecting long-term anti-thrombotic therapy after acute ischaemic stroke. QJM, 2003, 96(2): 143-153.
15. 娄昕, 何建风, 马林. 脑血管病医学影像人工智能研究现状与展望. 中华放射学杂志, 2020, 54(5): 389-392.
16. Li X, Wu M, Sun C, et al. Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients. Eur J Neurol, 2020, 27(8): 1656-1663.
17. De Ryck A, Brouns R, Geurden M, et al. Risk factors for poststroke depression: identification of inconsistencies based on a systematic review. J Geriatr Psychiatry Neurol, 2014, 27(3): 147-158.
18. Schepers V, Post M, Visser-Meily A, et al. Prediction of depressive symptoms up to three years post-stroke. J Rehabil Med, 2009, 41(11): 930-935.
19. Liu R, Yue Y, Jiang H, et al. A risk prediction model for post-stroke depression in Chinese stroke survivors based on clinical and socio-psychological features. Oncotarget, 2017, 8(38): 62891-62899.
20. Tung F, Mori G. Deep neural network compression by in-parallel pruning-quantization. IEEE Trans Pattern Anal Mach Intell, 2020, 42(3): 568-579.
21. Huang G, Liu Z, Pleiss G, et al. Convolutional networks with dense connectivity. IEEE Trans Pattern Anal Mach Intell, 2022, 44(12): 8704-8716.
22. Heo J, Yoon JG, Park H, et al. Machine learning-based model for prediction of outcomes in acute stroke. Stroke, 2019, 50(5): 1263-1265.
23. Hilbert A, Ramos LA, van Os HJA, et al. Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke. Comput Biol Med, 2019, 115: 103516.
24. Cheon S, Kim J, Lim J. The use of deep learning to predict stroke patient mortality. Int J Environ Res Public Health, 2019, 16(11): 1876.
25. Lin WY, Chen CH, Tseng YJ, et al. Predicting post-stroke activities of daily living through a machine learning-based approach on initiating rehabilitation. Int J Med Inform, 2018, 111: 159-164.
26. Nielsen A, Hansen MB, Tietze A, et al. Prediction of tissue outcome and assessment of treatment effect in acute ischemic stroke using deep learning. Stroke, 2018, 49(6): 1394-1401.
27. Tekkeşin Aİ. Artificial intelligence in healthcare: past, present and future. Anatol J Cardiol, 2019, 22(Suppl 2): 8-9.

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Research progress of diagnosis and prediction system of stroke based on artificial intelligence

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