机器学习在脑卒中诊断与治疗中的应用进展

doi:10.3969/j.issn.1672-6731.2023.01.002

摘要/Abstract

摘要： 随着人工智能技术的迅速发展，机器学习算法广泛应用于脑卒中诊断、治疗、预后各阶段，并表现出巨大的应用潜力。本文从机器学习算法研究，机器学习算法在脑卒中诊断与分类诊断、结局预测、风险因素识别等方面总结机器学习算法在脑卒中诊断与治疗中的应用现状并展望未来发展方向。

关键词: 卒中, 机器学习, 综述

Abstract: With the rapid development of artificial intelligence (AI), machine learning (ML) algorithms has been widely used in stroke diagnosis, treatment and prognosis, and has shown great application potential. This paper summarizes the application status of ML algorithms in stroke research from the aspects of ML algorithms research, ML algorithms in stroke diagnosis and classification diagnosis, outcome prediction, risk factor identification and so on, and looks into the future development direction.

Key words: Stroke, Machine learning, Review

高小波, 舒畅, 李祥飞, 骆达, 葛乐. 机器学习在脑卒中诊断与治疗中的应用进展[J]. 中国现代神经疾病杂志, 2023, 23(1): 5-8.

GAO Xiao-bo, SHU Chang, LI Xiang-fei, LUO Da, GE Le. Advances in machine learning for the diagnosis and treatment of stroke[J]. Chinese Journal of Contemporary Neurology and Neurosurgery, 2023, 23(1): 5-8.

https://journal11.magtechjournal.com/cjcnn/CN/Y2023/V23/I1/5

参考文献

[1] Samuel AL. Some studies in machine learning using the game of checkers[J]. IBM J Res Dev, 2000, 44:206-226.
[2] Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis[J]. Cancer Inform, 2007, 2:59-77.
[3] GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990-2019:a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet Neurol, 2021, 20:795-820.
[4] Lee H, Lee EJ, Ham S, Lee HB, Lee JS, Kwon SU, Kim JS, Kim N, Kang DW. Machine learning approach to identify stroke within 4.5 hours[J]. Stroke, 2020, 51:860-866.
[5] Mainali S, Darsie ME, Smetana KS. Machine learning in action:stroke diagnosis and outcome prediction[J]. Front Neurol, 2021, 12:734345.
[6] Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, Moore GJ. Advanced machine learning in action:identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration[J]. NPJ Digit Med, 2018, 1:9.
[7] Ho KC, Speier W, Zhang H, Scalzo F, El-Saden S, Arnold CW. A machine learning approach for classifying ischemic stroke onset time from imaging[J]. IEEE Trans Med Imaging, 2019, 38:1666-1676.
[8] Park E, Lee K, Han T, Nam HS. Automatic grading of stroke symptoms for rapid assessment using optimized machine learning and 4-limb kinematics:clinical validation study[J]. J Med Internet Res, 2020, 22:e20641.
[9] Hinton GE, Osindero S, Teh YW. A fast learning algorithm for deep belief nets[J]. Neural Comput, 2006, 18:1527-1554.
[10] Saba L, Biswas M, Kuppili V, Cuadrado Godia E, Suri HS, Edla DR, Omerzu T, Laird JR, Khanna NN, Mavrogeni S, Protogerou A, Sfikakis PP, Viswanathan V, Kitas GD, Nicolaides A, Gupta A, Suri JS. The present and future of deep learning in radiology[J]. Eur J Radiol, 2019, 114:14-24.
[11] Ramos-Murguialday A, Broetz D, Rea M, Läer L, Yilmaz O, Brasil FL, Liberati G, Curado MR, Garcia-Cossio E, Vyziotis A, Cho W, Agostini M, Soares E, Soekadar S, Caria A, Cohen LG, Birbaumer N. Brain-machine interface in chronic stroke rehabilitation:a controlled study[J]. Ann Neurol, 2013, 74:100-108.
[12] Murray NM, Unberath M, Hager GD, Hui FK. Artificial intelligence to diagnose ischemic stroke and identify large vessel occlusions:a systematic review[J]. J Neurointerv Surg, 2020, 12:156-164.
[13] Tang Z, Xu Y, Jin L, Aibaidula A, Lu J, Jiao Z, Wu J, Zhang H, Shen D. Deep learning of imaging phenotype and genotype for predicting overall survival time of glioblastoma patients[J]. IEEE Trans Med Imaging, 2020, 39:2100-2109.
[14] Li L, Wei M, Liu B, Atchaneeyasakul K, Zhou F, Pan Z, Kumar SA, Zhang JY, Pu Y, Liebeskind DS, Scalzo F. Deep learning for hemorrhagic lesion detection and segmentation on brain CT images[J]. IEEE J Biomed Health Inform, 2021, 25:1646-1659.
[15] Ni Y, Alwell K, Moomaw CJ, Woo D, Adeoye O, Flaherty ML, Ferioli S, Mackey J, De Los Rios La Rosa F, Martini S, Khatri P, Kleindorfer D, Kissel BM. Towards phenotyping stroke:leveraging data from a large-scale epidemiological study to detect stroke diagnosis[J]. PLoS One, 2018, 13:e0192586.
[16] Maier O, Schröder C, Forkert ND, Martinetz T, Handels H. Correction:classifiers for ischemic stroke lesion segmentation:a comparison study[J]. PLoS One, 2016, 11:e0149828.
[17] Zhu H, Jiang L, Zhang H, Luo L, Chen Y, Chen Y. An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging[J]. Neuroimage Clin, 2021, 31:102744.
[18] Arac A. Machine learning for 3D kinematic analysis of movements in neurorehabilitation[J]. Curr Neurol Neurosci Rep, 2020, 20:29.
[19] Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, Wang Y, Dong Q, Shen H, Wang Y. Artificial intelligence in healthcare:past, present and future[J]. Stroke Vasc Neurol, 2017, 2:230-243.
[20] Yahiya S, Yousif A, Bakri M. Classification of ischemic stroke using machine learning algorithms[J]. Int J Comput App, 2016, 149:26-31.
[21] Mohr JP, Caplan LR, Melski JW, Goldstein RJ, Duncan GW, Kistler JP, Pessin MS, Bleich HL. The harvard cooperative stroke registry:a prospective registry[J]. Neurology, 1978, 28:754-762.
[22] Sacco RL, Ellenberg JH, Mohr JP, Tatemichi TK, Hier DB, Price TR, Wolf PA. Infarcts of undetermined cause:the NINCDS Stroke Data Bank[J]. Ann Neurol, 1989, 25:382-390.
[23] Washington HH, Glaser KR, Ifejika NL. CE:acute ischemic stroke[J]. Am J Nurs, 2021, 121:26-33.
[24] Inamdar MA, Raghavendra U, Gudigar A, Chakole Y, Hegde A, Menon GR, Barua P, Palmer EE, Cheong KH, Chan WY, Ciaccio EJ, Acharya UR. A review on computer aided diagnosis of acute brain stroke[J]. Sensors (Basel), 2021, 21:8507.
[25] Ho KC, Speier W, El-Saden S, Arnold CW. Classifying acute ischemic stroke onset time using deep imaging features[J]. AMIA Annu Symp Proc, 2018, 2017:892-901.
[26] Yu Y, Guo D, Lou M, Liebeskind D, Scalzo F. Prediction of hemorrhagic transformation severity in acute stroke from source perfusion MRI[J]. IEEE Trans Biomed Eng, 2018, 65:2058-2065.
[27] Fatahzadeh M, Glick M. Stroke:epidemiology, classification, risk factors, complications, diagnosis, prevention, and medical and dental management[J]. Oral Surg Oral Med Oral Pathol Oral Radiol Endod, 2006, 102:180-191.
[28] Garg R, Oh E, Naidech A, Kording K, Prabhakaran S. Automating ischemic stroke subtype classification using machine learning and natural language processing[J]. J Stroke Cerebrovasc Dis, 2019, 28:2045-2051.
[29] Govindarajan P, Soundarapandian RK, Gandomi AH, Patan R, Jayaraman P, Manikandan R. Classification of stroke disease using machine learning algorithms[J]. Neural Comput App, 2020, 32:817-828.
[30] Asadi H, Dowling R, Yan B, Mitchell P. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy[J]. PLoS One, 2014, 9:e88225.
[31] Alaka SA, Menon BK, Brobbey A, Williamson T, Goyal M, Demchuk AM, Hill MD, Sajobi TT. Functional outcome prediction in ischemic stroke:a comparison of machine learning algorithms and regression models[J]. Front Neurol, 2020, 11:889.
[32] Matsumoto K, Nohara Y, Soejima H, Yonehara T, Nakashima N, Kamouchi M. Stroke prognostic scores and data-driven prediction of clinical outcomes after acute ischemic stroke[J]. Stroke, 2020, 51:1477-1483.
[33] Lin CH, Hsu KC, Johnson KR, Fann YC, Tsai CH, Sun Y, Lien LM, Chang WL, Chen PL, Lin CL, Hsu CY; Taiwan Stroke Registry Investigators. Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry[J]. Comput Methods Programs Biomed, 2020, 190:105381.
[34] Sirsat MS, Fermé E, Câmara J. Machine learning for brain stroke:a review[J]. J Stroke Cerebrovasc Dis, 2020, 29:105162.
[35] Campbell BCV, De Silva DA, Macleod MR, Coutts SB, Schwamm LH, Davis SM, Donnan GA. Ischaemic stroke[J]. Nat Rev Dis Primers, 2019, 5:70.
[36] Campbell BCV, Khatri P. Stroke[J]. Lancet, 2020, 396:129-142.
[37] Pelcher I, Puzo C, Tripodis Y, Aparicio HJ, Steinberg EG, Phelps A, Martin B, Palmisano JN, Vassey E, Lindbergh C, McKee AC, Stein TD, Killiany RJ, Au R, Kowall NW, Stern RA, Mez J, Alosco ML. Revised framingham stroke risk profile:association with cognitive status and MRI-derived volumetric measures[J]. J Alzheimers Dis, 2020, 78:1393-1408.
[38] Yang Y, Zheng J, Du Z, Li Y, Cai Y. Accurate prediction of stroke for hypertensive patients based on medical big data and machine learning algorithms:retrospective study[J]. JMIR Med Inform, 2021, 9:e30277.
[39] Scrutinio D, Ricciardi C, Donisi L, Losavio E, Battista P, Guida P, Cesarelli M, Pagano G, D'Addio G. Machine learning to predict mortality after rehabilitation among patients with severe stroke[J]. Sci Rep, 2020, 10:20127.
[40] Bacchi S, Oakden-Rayner L, Menon DK, Jannes J, Kleinig T, Koblar S. Stroke prognostication for discharge planning with machine learning:a derivation study[J]. J Clin Neurosci, 2020, 79:100-103.
[41] Sakai K, Yamada K. Machine learning studies on major brain diseases:5-year trends of 2014-2018[J]. Jpn J Radiol, 2019, 37:34-72.
[42] Vayena E, Blasimme A, Cohen IG. Machine learning in medicine:addressing ethical challenges[J]. PLoS Med, 2018, 15:e1002689.
[43] McGraw G, Bonett R, Shepardson V, Figueroa H. The top 10 risks of machine learning security[J]. Comput, 2020, 53:57-61.

选择文件类型/文献管理软件名称

选择包含的内容

2 机器学习在脑卒中诊断与治疗中的应用进展

Advances in machine learning for the diagnosis and treatment of stroke

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	佟志勇. 颅内动脉瘤与脑血管重建术[J]. 中国现代神经疾病杂志, 2024, 24(8): 595-598.
[2]	佟志勇. 脑血管重建术历史沿革[J]. 中国现代神经疾病杂志, 2024, 24(8): 599-605.
[3]	金凌骥, 胡俊文, 李寅, 王林. 复杂颅内动脉瘤的颅内-颅内血管搭桥术[J]. 中国现代神经疾病杂志, 2024, 24(8): 606-612.
[4]	刘佩玺, 史源, 朱巍. 血流导向时代颅内全域脑血管搭桥术治疗颅内动脉瘤之应用方略[J]. 中国现代神经疾病杂志, 2024, 24(8): 613-618.
[5]	孟凡刚, 许天奇, 季玉陈, 张树新. 小脑齿状核脑深部电刺激术对脑卒中后运动功能的改善作用[J]. 中国现代神经疾病杂志, 2024, 24(7): 507-509.
[6]	张峰, 孟凡刚. 帕金森病自主神经功能障碍丘脑底核脑深部电刺激术治疗进展[J]. 中国现代神经疾病杂志, 2024, 24(7): 510-515.
[7]	吴桂智, 田宏. Meige综合征脑深部电刺激术治疗进展[J]. 中国现代神经疾病杂志, 2024, 24(7): 516-521.
[8]	孟凡刚, 张建国. 耳后磨骨槽技术在脑深部电刺激术中的应用[J]. 中国现代神经疾病杂志, 2024, 24(7): 522-524.
[9]	陈琳, 潘华. 功能性震颤的电生理诊断[J]. 中国现代神经疾病杂志, 2024, 24(7): 573-578.
[10]	魏俊吉, 常健博. 深入基层医院普及神经外科重症管理理念[J]. 中国现代神经疾病杂志, 2024, 24(6): 403-406.
[11]	史若琳, 高秀洁, 崔家宁, 邵妍, 谢璐霜, 刘毅. 5-羟色胺与帕金森病非运动症状研究进展[J]. 中国现代神经疾病杂志, 2024, 24(6): 497-501.
[12]	李海峰. 在进入靶向治疗的时代激素仍然是重症肌无力治疗的基础[J]. 中国现代神经疾病杂志, 2024, 24(5): 295-307.
[13]	许鸿嘉, 舒崖清, 王蘋, 李蕃, 许顺良, 邱伟, 马晓宇. 抗水通道蛋白4抗体阳性视神经脊髓炎谱系疾病诊断与治疗进展[J]. 中国现代神经疾病杂志, 2024, 24(5): 308-314.
[14]	黄瑀, 廖金池, 舒崖清. 抗N-甲基-D-天冬氨酸受体脑炎睡眠障碍研究进展[J]. 中国现代神经疾病杂志, 2024, 24(5): 315-319.
[15]	刘彩云, 金涛. 树突状细胞在多发性硬化免疫治疗中的应用[J]. 中国现代神经疾病杂志, 2024, 24(5): 320-330.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

2 机器学习在脑卒中诊断与治疗中的应用进展

Advances in machine learning for the diagnosis and treatment of stroke

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价