Research progress of artificial intelligence in stroke management

doi:10.3969/j.issn.1672-6731.2021.01.004

Abstract

Abstract:

As a new science and technology, artificial intelligence (AI) has been applied to the prevention, diagnosis, treatment and rehabilitation of stroke, and shows great potential. The combination of artificial intelligence and big data can accurately identify high-risk subjects, classify stroke subtype, and assist in the formulate of therapeutic strategies in acute period and prevention strategy of stroke. Sequentially, it can improve the effect of rehabilitation. This paper reviews the research progress of artificial intelligence in the prevention, diagnosis, treatment and rehabilitation of stroke.

Key words: Stroke, Artificial intelligence, Primary prevention, Secondary prevention, Neurological rehabilitation, Review

摘要：

人工智能作为一种新兴技术，已应用于脑卒中预防、诊治和康复等多领域，并突显出巨大的潜力。人工智能与大数据相结合，可用于脑卒中高危人群的精准识别、自动化病因分型、辅助脑卒中急性期和二级预防策略的制定，从而提高脑卒中患者康复治疗效果。本文综述人工智能在脑卒中预防、诊治和康复中的研究进展。

关键词: 卒中, 人工智能, 一级预防, 二级预防, 神经康复, 综述

YU Chang-shen, WU Jia-ling. Research progress of artificial intelligence in stroke management[J]. Chinese Journal of Contemporary Neurology and Neurosurgery, 2021, 21(1): 14-20.

于长申, 巫嘉陵. 人工智能在脑卒中管理中的研究进展[J]. 中国现代神经疾病杂志, 2021, 21(1): 14-20.

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References

[1] GBD 2016 Stroke Collaborators. Global, regional, and national burden of stroke, 1990-2016:a systematic analysis for the Global Burden of Disease Study 2016[J]. Lancet Neurol, 2019, 18:439-458.
[2] Kaji R. Global burden of neurological diseases highlights stroke[J]. Nat Rev Neurol, 2019, 15:371-372.
[3] Krishnamurthi RV, Ikeda T, Feigin VL. Global, regional and country-specific burden of ischaemic stroke, intracerebral haemorrhage and subarachnoid haemorrhage:a systematic analysis of the global burden of disease study 2017[J]. Neuroepidemiology, 2020, 54:171-179.
[4] Wang W, Jiang B, Sun H, Ru X, Sun D, Wang L, Wang L, Jiang Y, Li Y, Wang Y, Chen Z, Wu S, Zhang Y, Wang D, Wang Y, Feigin VL; NESS-China Investigators. Prevalence, incidence, and mortality of stroke in China:results from a nationwide population-based survey of 480 687 adults[J]. Circulation, 2017, 135:759-771.
[5] Sahraeian SME, Liu R, Lau B, Podesta K, Mohiyuddin M, Lam HYK. Deep convolutional neural networks for accurate somatic mutation detection[J]. Nat Commun, 2019, 10:1041.
[6] Shortliffe EH, Sepúlveda MJ. Clinical decision support in the era of artificial intelligence[J]. JAMA, 2018, 320:2199-2200.
[7] Panesar S, Cagle Y, Chander D, Morey J, Fernandez-Miranda J, Kliot M. Artificial intelligence and the future of surgical robotics[J]. Ann Surg, 2019, 270:223-226.
[8] Langlotz CP, Allen B, Erickson BJ, Kalpathy-Cramer J, Bigelow K, Cook TS, Flanders AE, Lungren MP, Mendelson DS, Rudie JD, Wang G, Kandarpa K. A roadmap for foundational research on artificial intelligence in medical imaging:from the 2018 NIH/RSNA/ACR/The Academy Workshop[J]. Radiology, 2019, 291:781-791.
[9] Griffen EJ, Dossetter AG, Leach AG, Montague S. Can we accelerate medicinal chemistry by augmenting the chemist with big data and artificial intelligence[J]? Drug Discov Today, 2018, 23:1373-1384.
[10] Feigin VL, Roth GA, Naghavi M, Parmar P, Krishnamurthi R, Chugh S, Mensah GA, Norrving B, Shiue I, Ng M, Estep K, Cercy K, Murray CJL, Forouzanfar MH; Global Burden of Diseases, Injuries and Risk Factors Study 2013 and Stroke Experts Writing Group. Global burden of stroke and risk factors in 188 countries, during 1990-2013:a systematic analysis for the Global Burden of Disease Study 2013[J]. Lancet Neurol, 2016, 15:913-924.
[11] Zhang F, Yu JM, Ding RJ, Zhang LJ, Li SC, Zhan YQ, Hu DY. Analysis of risk factors and cognition status of stroke in residents of Beijing[J]. Zhongguo Shi Yong Nei Ke Za Zhi, 2011, 31:116-118.[张芬,余金明,丁荣晶,张李军,李社昌,战义强,胡大一.北京市居民脑卒中危险因素及认知情况分析[J].中国实用内科杂志, 2011, 31:116-118.]
[12] Zhou Y, Li JM, Liu Y. Investigation on awareness for risk factors of stroke in primary farmers[J]. Xian Dai Sheng Wu Yi Xue Jin Zhan, 2009, 9:3722-3725.[周毅,李建明,刘燕.基层农民对脑卒中危险因素知晓率的调查分析[J].现代生物医学进展, 2009, 9:3722-3725.]
[13] Miao F, Fu N, Zhang YT, Ding XR, Hong X, He Q, Li Y. A novel continuous blood pressure estimation approach based on data mining techniques[J]. IEEE J Biomed Health Inform, 2017, 21:1730-1740.
[14] Tsoi KKF, Chan NB, Yiu KKL, Poon SKS, Lin B, Ho K. Machine learning clustering for blood pressure variability applied to systolic blood pressure intervention trial (SPRINT) and the Hong Kong Community Cohort[J]. Hypertension, 2020, 76:569-576.
[15] Koren G, Nordon G, Radinsky K, Shalev V. Machine learning of big data in gaining insight into successful treatment of hypertension[J]. Pharmacol Res Perspect, 2018, 6:e00396.
[16] Koshimizu H, Kojima R, Okuno Y. Future possibilities for artificial intelligence in the practical management of hypertension[J]. Hypertens Res, 2020, 43:1327-1337.
[17] Li J, Huang J, Zheng L, Li X. Application of artificial intelligence in diabetes education and management:present status and promising prospect[J]. Front Public Health, 2020, 8:173.
[18] Attia ZI, Noseworthy PA, Lopez-Jimenez F, Asirvatham SJ, Deshmukh AJ, Gersh BJ, Carter RE, Yao X, Rabinstein AA, Erickson BJ, Kapa S, Friedman PA. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm:a retrospective analysis of outcome prediction[J]. Lancet, 2019, 394:861-867.
[19] Asgari S, Mehrnia A, Moussavi M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine[J]. Comput Biol Med, 2015, 60:132-142.
[20] Meneu T, Martínez-Romero Á, Martínez-Millana A, Guillén S. An integrated advanced communication and coaching platform for enabling personalized management of chronic cardiovascular diseases[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2011, 2011:1563-1566.
[21] Logallo N, Novotny V, Assmus J, Kvistad CE, Alteheld L, Rønning OM, Thommessen B, Amthor KF, Ihle-Hansen H, Kurz M, Tobro H, Kaur K, Stankiewicz M, Carlsson M, Morsund Å, Idicula T, Aamodt AH, Lund C, Næss H, Waje-Andreassen U, Thomassen L. Tenecteplase versus alteplase for management of acute ischaemic stroke (NOR-TEST):a phase 3, randomised, open-label, blinded endpoint trial[J]. Lancet Neurol, 2017, 16:781-788.
[22] Yang P, Zhang Y, Zhang L, Zhang Y, Treurniet KM, Chen W, Peng Y, Han H, Wang J, Wang S, Yin C, Liu S, Wang P, Fang Q, Shi H, Yang J, Wen C, Li C, Jiang C, Sun J, Yue X, Lou M, Zhang M, Shu H, Sun D, Liang H, Li T, Guo F, Ke K, Yuan H, Wang G, Yang W, Shi H, Li T, Li Z, Xing P, Zhang P, Zhou Y, Wang H, Xu Y, Huang Q, Wu T, Zhao R, Li Q, Fang Y, Wang L, Lu J, Li Y, Fu J, Zhong X, Wang Y, Wang L, Goyal M, Dippel DWJ, Hong B, Deng B, Roos YBWEM, Majoie CBLM, Liu J; DIRECT-MT Investigators. Endovascular thrombectomy with or without intravenous alteplase in acute stroke[J]. N Engl J Med, 2020, 382:1981-1993.
[23] Chinese Society of Neurology, Chinese Stroke Society. Chinese guideline for diagnosis and treatment of acute ischemic stroke 2018[J]. Zhonghua Shen Jing Ke Za Zhi, 2018, 51:666-682.[中华医学会神经病学分会,中华医学会神经病学分会脑血管病学组.中国急性缺血性脑卒中诊治指南2018[J].中华神经科杂志, 2018, 51:666-682.]
[24] Abedi V, Goyal N, Tsivgoulis G, Hosseinichimeh N, Hontecillas R, Bassaganya-Riera J, Elijovich L, Metter JE, Alexandrov AW, Liebeskind DS, Alexandrov AV, Zand R. Novel screening tool for stroke using artificial neural network[J]. Stroke, 2017, 48:1678-1681.
[25] Guo L, Abbosh A. Stroke localization and classification using microwave tomography with k-means clustering and support vector machine[J]. Bioelectromagnetics, 2018, 39:312-324.
[26] National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue plasminogen activator for acute ischemic stroke[J]. N Engl J Med, 1995, 333:1581-1587.
[27] Hacke W, Kaste M, Bluhmki E, Brozman M, Dávalos A, Guidetti D, Larrue V, Lees KR, Medeghri Z, Machnig T, Schneider D, von Kummer R, Wahlgren N, Toni D; ECASS Investigators. Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke[J]. N Engl J Med, 2008, 359:1317-1329.
[28] Lees KR, Emberson J, Blackwell L, Bluhmki E, Davis SM, Donnan GA, Grotta JC, Kaste M, von Kummer R, Lansberg MG, Lindley RI, Lyden P, Murray GD, Sandercock PA, Toni D, Toyoda K, Wardlaw JM, Whiteley WN, Baigent C, Hacke W, Howard G; Stroke Thrombolysis Trialists'Collaborators Group. Effects of alteplase for acute stroke on the distribution of functional outcomes:a pooled analysis of 9 trials[J]. Stroke, 2016, 47:2373-2379.
[29] 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.
[30] Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, McTaggart RA, Torbey MT, Kim-Tenser M, Leslie-Mazwi T, Sarraj A, Kasner SE, Ansari SA, Yeatts SD, Hamilton S, Mlynash M, Heit JJ, Zaharchuk G, Kim S, Carrozzella J, Palesch YY, Demchuk AM, Bammer R, Lavori PW, Broderick JP, Lansberg MG; DEFUSE 3 Investigators. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging[J]. N Engl J Med, 2018, 378:708-718.
[31] Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, Yavagal DR, Ribo M, Cognard C, Hanel RA, Sila CA, Hassan AE, Millan M, Levy EI, Mitchell P, Chen M, English JD, Shah QA, Silver FL, Pereira VM, Mehta BP, Baxter BW, Abraham MG, Cardona P, Veznedaroglu E, Hellinger FR, Feng L, Kirmani JF, Lopes DK, Jankowitz BT, Frankel MR, Costalat V, Vora NA, Yoo AJ, Malik AM, Furlan AJ, Rubiera M, Aghaebrahim A, Olivot JM, Tekle WG, Shields R, Graves T, Lewis RJ, Smith WS, Liebeskind DS, Saver JL, Jovin TG; DAWN Trial Investigators. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct[J]. N Engl J Med, 2018, 378:11-21.
[32] Saver JL, Goyal M, Bonafe A, Diener HC, Levy EI, Pereira VM, Albers GW, Cognard C, Cohen DJ, Hacke W, Jansen O, Jovin TG, Mattle HP, Nogueira RG, Siddiqui AH, Yavagal DR, Baxter BW, Devlin TG, Lopes DK, Reddy VK, du Mesnil de Rochemont R, Singer OC, Jahan R; SWIFT PRIME Investigators. Stent-retriever thrombectomy after intravenous t-PA vs. t-PA alone in stroke[J]. N Engl J Med, 2015, 372:2285-2295.
[33] Saver JL, Jahan R, Levy EI, Jovin TG, Baxter B, Nogueira RG, Clark W, Budzik R, Zaidat OO; SWIFT Trialists. Solitaire flow restoration device versus the Merci Retriever in patients with acute ischaemic stroke (SWIFT):a randomised, parallel-group, non-inferiority trial[J]. Lancet, 2012, 380:1241-1249.
[34] Sheth SA, Lopez-Rivera V, Barman A, Grotta JC, Yoo AJ, Lee S, Inam ME, Savitz SI, Giancardo L. Machine learning-enabled automated determination of acute ischemic core from computed tomography angiography[J]. Stroke, 2019, 50:3093-3100.
[35] Kim YC, Lee JE, Yu I, Song HN, Baek IY, Seong JK, Jeong HG, Kim BJ, Nam HS, Chung JW, Bang OY, Kim GM, Seo WK. Evaluation of diffusion lesion volume measurements in acute ischemic stroke using encoder-decoder convolutional network[J]. Stroke, 2019, 50:1444-1451.
[36] 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.
[37] Chung JW, Kim YC, Cha J, Choi EH, Kim BM, Seo WK, Kim GM, Bang OY. Characterization of clot composition in acute cerebral infarct using machine learning techniques[J]. Ann Clin Transl Neurol, 2019, 6:739-747.
[38] Chen YX, Jiang Y, Li ZX, Pan YS, Ji RJ, Wang YL, Wang YJ, Wang C. Current status of medication compliance in secondary prevention for ischemic stroke or transient ischemic attack[J]. Zhongguo Zu Zhong Za Zhi, 2018, 13:686-691.[陈艳雪,姜悦,李子孝,潘岳松, 冀瑞俊,王伊龙,王拥军,王晨.中国急性缺血性卒中及短暂性脑缺血发作二级预防药物依从性的现状[J].中国卒中杂志, 2018, 13:686-691.]
[39] Schweitzer M, Hoerbst A. Robotic assistance in medication management:development and evaluation of a prototype[J]. Stud Health Technol Inform, 2016, 225:422-426.
[40] Chilamkurthy S, Ghosh R, Tanamala S, Biviji M, Campeau NG, Venugopal VK, Mahajan V, Rao P, Warier P. Deep learning algorithms for detection of critical findings in head CT scans:a retrospective study[J]. Lancet, 2018, 392:2388-2396.
[41] Ye H, Gao F, Yin Y, Guo D, Zhao P, Lu Y, Wang X, Bai J, Cao K, Song Q, Zhang H, Chen W, Guo X, Xia J. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network[J]. Eur Radiol, 2019, 29:6191-6201.
[42] Dhar R, Falcone GJ, Chen Y, Hamzehloo A, Kirsch EP, Noche RB, Roth K, Acosta J, Ruiz A, Phuah CL, Woo D, Gill TM, Sheth KN, Lee JM. Deep learning for automated measurement of hemorrhage and perihematomal edema in supratentorial intracerebral hemorrhage[J]. Stroke, 2020, 51:648-651.
[43] Ironside N, Chen CJ, Mutasa S, Sim JL, Marfatia S, Roh D, Ding D, Mayer SA, Lignelli A, Connolly ES. Fully automated segmentation algorithm for hematoma volumetric analysis in spontaneous intracerebral hemorrhage[J]. Stroke, 2019, 50:3416-3423.
[44] Ironside N, Chen CJ, Mutasa S, Sim JL, Ding D, Marfatiah S, Roh D, Mukherjee S, Johnston KC, Southerland AM, Mayer SA, Lignelli A, Connolly ES. Fully automated segmentation algorithm for perihematomal edema volumetry after spontaneous intracereral hemorrhage[J]. Stroke, 2020, 51:815-823.
[45] Alan N, Lee P, Ozpinar A, Gross BA, Jankowitz BT. Robotic stereotactic assistance (ROSA) utilization for minimally invasive placement of intraparenchymal hematoma and intraventricular catheters[J]. World Neurosurg, 2017, 108:996.
[46] Sun X, Tao YQ, Xu F, Yang XW, Wang Y, Xu MT, Wang JH, Fei S, Wang TT. Superiority and feasibility of Robot of Stereotactic Assistant system for the treatment of hypertensive cerebral hemorrhage[J]. Zhongguo Wei Qin Xi Shen Jing Wai Ke Za Zhi, 2017, 22:51-53.[孙霄,陶英群,许峰,杨兴旺,王宇,徐梦婷,王俊和,费爽,王婷婷. ROSA辅助治疗高血压脑出血的优越性及可行性研究[J].中国微侵袭神经外科杂志, 2017, 22:51-53.]
[47] Xu F, Tao YQ, Sun X, Yang XW, Wang Y, Xu MT, Wang JH, Fei S, Wang TT. Robot of Stereotactic Assistant system assisted stereotactic surgery for the treatment of hypertensive brainstem hemorrhage[J]. Zhongguo Wei Qin Xi Shen Jing Wai Ke Za Zhi, 2017, 22:54-56.[许峰,陶英群,孙霄,杨兴旺,王宇,徐梦婷,王俊和,费爽,王婷婷. ROSA辅助定向手术治疗高血压性脑干出血[J].中国微侵袭神经外科杂志, 2017, 22:54-56.]
[48] Wang T, Zhao QJ, Gu JW, Shi TJ, Yuan X, Wang J, Cui SJ. Neurosurgery medical robot remebot for the treatment of 17 patients with hypertensive intracerebral hemorrhage[J]. Int J Med Robot, 2019, 15:e2024.
[49] Seciety of Neurology of Chinese Medical Association, Neurorehabilitation group of Seciety of Neurology of Chinese Medical Association, Cerebrovascular group of Seciety of Neurology of Chinese Medical Association. Chinese guideline for early rehabilitation of stroke[J]. Zhonghua Shen Jing Ke Za Zhi, 2017, 50:405-412.[中华医学会神经病学分会,中华医学会神经病学分会神经康复学组,中华医学会神经病学分会脑血管病学组.中国脑卒中早期康复治疗指南[J].中华神经科杂志, 2017, 50:405-412.]
[50] Krebs HI, Krams M, Agrafiotis DK, DiBernardo A, Chavez JC, Littman GS, Yang E, Byttebier G, Dipietro L, Rykman A, McArthur K, Hajjar K, Lees KR, Volpe BT. Robotic measurement of arm movements after stroke establishes biomarkers of motor recovery[J]. Stroke, 2014, 45:200-204.
[51] Bengtson MC, Mrotek LA, Stoeckmann T, Ghez C, Scheidt RA. The arm motion detection (AMD) test[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2014:5349-5352.
[52] Deblock-Bellamy A, Batcho CS, Mercier C, Blanchette AK. Quantification of upper limb position sense using an exoskeleton and a virtual reality display[J]. J Neuroeng Rehabil, 2018, 15:24.
[53] Semrau JA, Herter TM, Scott SH, Dukelow SP. Inter-rater reliability of kinesthetic measurements with the KINARM robotic exoskeleton[J]. J Neuroeng Rehabil, 2017, 14:42.
[54] Park JH, Kim Y, Lee KJ, Yoon YS, Kang SH, Kim H, Park HS. Artificial neural network learns clinical assessment of spasticity in modified Ashworth scale[J]. Arch Phys Med Rehabil, 2019, 100:1907-1915.
[55] Ghahramani Z. Probabilistic machine learning and artificial intelligence[J]. Nature, 2015, 521:452-459.
[56] Chaudhary U, Birbaumer N, Ramos-Murguialday A. Brain-computer interfaces for communication and rehabilitation[J]. Nat Rev Neurol, 2016, 12:513-525.
[57] Coscia M, Wessel MJ, Chaudary U, Millán JDR, Micera S, Guggisberg A, Vuadens P, Donoghue J, Birbaumer N, Hummel FC. Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke[J]. Brain, 2019, 142:2182-2197.
[58] Mrachacz-Kersting N, Jiang N, Stevenson AJ, Niazi IK, Kostic V, Pavlovic A, Radovanovic S, Djuric-Jovicic M, Agosta F, Dremstrup K, Farina D. Efficient neuroplasticity induction in chronic stroke patients by an associative brain-computer interface[J]. J Neurophysiol, 2016, 115:1410-1421.
[59] 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.
[60] Lo AC, Guarino PD, Richards LG, Haselkorn JK, Wittenberg GF, Federman DG, Ringer RJ, Wagner TH, Krebs HI, Volpe BT, Bever CT Jr, Bravata DM, Duncan PW, Corn BH, Maffucci AD, Nadeau SE, Conroy SS, Powell JM, Huang GD, Peduzzi P. Robot-assisted therapy for long-term upper-limb impairment after stroke[J]. N Engl J Med, 2010, 362:1772-1783.
[61] Veerbeek JM, Langbroek-Amersfoort AC, van Wegen EE, Meskers CG, Kwakkel G. Effects of robot-assisted therapy for the upper limb after stroke[J]. Neurorehabil Neural Repair, 2017, 31:107-121.
[62] Rodgers H, Bosomworth H, Krebs HI, van Wijck F, Howel D, Wilson N, Aird L, Alvarado N, Andole S, Cohen DL, Dawson J, Fernandez-Garcia C, Finch T, Ford GA, Francis R, Hogg S, Hughes N, Price CI, Ternent L, Turner DL, Vale L, Wilkes S, Shaw L. Robot assisted training for the upper limb after stroke (RATULS):a multicentre randomised controlled trial[J]. Lancet, 2019, 394:51-62.
[63] Mirelman A, Bonato P, Deutsch JE. Effects of training with a robot-virtual reality system compared with a robot alone on the gait of individuals after stroke[J]. Stroke, 2009, 40:169-174.
[64] Faria AL, Cameirão MS, Couras JF, Aguiar JRO, Costa GM, Bermúdez I Badia S. Combined cognitive-motor rehabilitation in virtual reality improves motor outcomes in chronic stroke:a pilot study[J]. Front Psychol, 2018, 9:854.
[65] Cano Porras D, Siemonsma P, Inzelberg R, Zeilig G, Plotnik M. Advantages of virtual reality in the rehabilitation of balance and gait:systematic review[J]. Neurology, 2018, 90:1017-1025.
[66] Laver KE, George S, Thomas S, Deutsch JE, Crotty M. Virtual reality for stroke rehabilitation[J]. Cochrane Database Syst Rev, 2015:CD008349.

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