Published May 30, 2022

Mayte Garcia-Perez  


As a burgeoning technology, artificial intelligence has been utilized in numerous domains, including stroke prevention, diagnosis, treatment, and rehabilitation, and has demonstrated considerable promise. The combination of artificial intelligence and big data can be utilized for accurate identification of stroke high-risk groups, automatic etiology classification, and assistance in the formulation of acute stroke and secondary prevention strategies, thereby enhancing the rehabilitation treatment effect for stroke patients. This article discusses the accomplishments made in artificial intelligence research for stroke prevention, diagnosis, treatment, and rehabilitation.



Artificial Intelligence, Stroke Prevention, Neurorehabilitation

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. Lancet Neurol 2019; 18(5):439-458. DOI: https://doi.org/10.1016/S1474-4422(19)30034-1

2. Katan M, Luft A. Global Burden of Stroke. Semin Neurol 2018; 38(2):208-211. DOI: https://doi.org/10.1055/s-0038-1649503

3. Rahbar MH, Medrano M, Diaz-Garelli F, Gonzalez Villaman C, Saroukhani S, Kim S, Tahanan A, Franco Y, Castro-Tejada G, Diaz SA, Hessabi M, Savitz SI. Younger age of stroke in low-middle income countries is related to healthcare access and quality. Ann Clin Transl Neurol 2022; 9(3):415-427. DOI: https://doi.org/10.1002/acn3.51507

4. Matizirofa L, Chikobvu D. Analysing and quantifying the effect of predictors of stroke direct costs in South Africa using quantile regression. BMC Public Health 2021; 21(1):1560. DOI: https://doi.org/10.1186/s12889-021-11592-0

5. Dias R, Torkamani A. Artificial intelligence in clinical and genomic diagnostics. Genome Med 2019; 11(1):70. DOI: https://doi.org/10.1186/s13073-019-0689-8

6. Juravle G, Boudouraki A, Terziyska M, Rezlescu C. Trust in artificial intelligence for medical diagnoses. Prog Brain Res 2020; 253:263-282. DOI: https://doi.org/10.1016/bs.pbr.2020.06.006

7. Stai B, Heller N, McSweeney S, Rickman J, Blake P, Vasdev R, Edgerton Z, Tejpaul R, Peterson M, Rosenberg J, Kalapara A, Regmi S, Papanikolopoulos N, Weight C. Public perceptions of artificial intelligence and robotics in medicine. J Endourol 2020; 34(10):1041-1048. DOI: https://doi.org/10.1089/end.2020.0137

8. Gore JC. Artificial intelligence in medical imaging. Magn Reson Imaging 2020; 68:A1-A4. DOI: https://doi.org/10.1016/j.mri.2019.12.006

9. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today 2021; 26(1):80-93. DOI: https://doi.org/10.1016/j.drudis.2020.10.010

10. Benke K, Benke G. Artificial intelligence and big data in public health. Int J Environ Res Public Health 2018; 15(12):2796. DOI: https://doi.org/10.3390/ijerph15122796

11. Khan ZF, Alotaibi SR. Applications of artificial intelligence and big data analytics in m-health: A healthcare system perspective. J Healthc Eng 2020; 2020:8894694. DOI: https://doi.org/10.1155/2020/8894694

12. Boehme AK, Esenwa C, Elkind MS. Stroke risk factors, genetics, and prevention. Circ Res 2017; 120(3):472-495. DOI: https://doi.org/10.1161/CIRCRESAHA.116.308398

13. Santhanam P, Ahima RS. Machine learning and blood pressure. J Clin Hypertens (Greenwich) 2019; 21(11):1735-1737. DOI: https://doi.org/10.1111/jch.13700

14. Chaikijurajai T, Laffin LJ, Tang WHW. Artificial intelligence and hypertension: recent advances and future outlook. Am J Hypertens 2020; 33(11):967-974. DOI: https://doi.org/10.1093/ajh/hpaa102

15. Tsoi K, Yiu K, Lee H, Cheng HM, Wang TD, Tay JC, Teo BW, Turana Y, Soenarta AA, Sogunuru GP, Siddique S, Chia YC, Shin J, Chen CH, Wang JG, Kario K; HOPE Asia Network. Applications of artificial intelligence for hypertension management. J Clin Hypertens (Greenwich) 2021; 23(3):568-574. DOI: https://doi.org/10.1111/jch.14180

16. Koshimizu H, Kojima R, Okuno Y. Future possibilities for artificial intelligence in the practical management of hypertension. Hypertens Res 2020; 43(12):1327-1337. DOI: https://doi.org/10.1038/s41440-020-0498-x

17. Padmanabhan S, Tran TQB, Dominiczak AF. Artificial intelligence in hypertension: seeing through a glass darkly. Circ Res 2021; 128(7):1100-1118. DOI: https://doi.org/10.1161/CIRCRESAHA.121.318106

18. Kario K, Harada N, Okura A. State-of-the-art rapid review of the current landscape of digital hypertension. Conn Health 2022; 1:46-58. DOI: https://doi.org/10.20517/ch.2022.02

19. Li J, Huang J, Zheng L, Li X. Application of artificial intelligence in diabetes education and management: present status and promising prospect. Front Public Health 2020; 8:173. DOI: https://doi.org/10.3389/fpubh.2020.00173

20. Makroum MA, Adda M, Bouzouane A, Ibrahim H. Machine learning and smart devices for diabetes management: Systematic review. Sensors (Basel) 2022; 22(5):1843. DOI: https://doi.org/10.3390/s22051843

21. Ellahham S. Artificial intelligence: The future for diabetes care. Am J Med 2020; 133(8):895-900. DOI: https://doi.org/10.1016/j.amjmed.2020.03.033

22. Lei N, Kareem M, Moon SK, Ciaccio EJ, Acharya UR, Faust O. Hybrid decision support to monitor atrial fibrillation for stroke prevention. Int J Environ Res Public Health 2021; 18(2):813. DOI: https://doi.org/10.3390/ijerph18020813

23. Nagarajan VD, Lee SL, Robertus JL, Nienaber CA, Trayanova NA, Ernst S. Artificial intelligence in the diagnosis and management of arrhythmias. Eur Heart J 2021; 42(38):3904-3916. DOI: https://doi.org/10.1093/eurheartj/ehab544

24. Asgari S, Mehrnia A, Moussavi M. Automatic detection of atrial fibrillation using stationary wavelet transform and support vector machine. Comput Biol Med 2015; 60:132-142. DOI: https://doi.org/10.1016/j.compbiomed.2015.03.005

25. Bivard A, Churilov L, Parsons M. Artificial intelligence for decision support in acute stroke - Current roles and potential. Nat Rev Neurol 2020; 16(10):575-585. DOI: https://doi.org/10.1038/s41582-020-0390-y

26. Sloane EB, Silva RJ. Artificial intelligence in medical devices and clinical decision support systems. Clin Eng Handbook 2020:556-568. DOI: https://doi.org/10.1016/B978-0-12-813467-2.00084-5

27. Nedeltchev K, Schwegler B, Haefeli T, Brekenfeld C, Gralla J, Fischer U, Arnold M, Remonda L, Schroth G, Mattle HP. Outcome of stroke with mild or rapidly improving symptoms. Stroke 2007; 38(9):2531-2535. DOI: https://doi.org/10.1161/STROKEAHA.107.482554

28. Musuka TD, Wilton SB, Traboulsi M, Hill MD. Diagnosis and management of acute ischemic stroke: Speed is critical. CMAJ 2015; 187(12):887-893. DOI: https://doi.org/10.1503/cmaj.140355

29. Hurford R, Sekhar A, Hughes TAT, Muir KW. Diagnosis and management of acute ischaemic stroke. Pract Neurol 2020; 20(4):304-316. DOI: https://doi.org/10.1136/practneurol-2020-002557

30. Sui B, Gao P. Imaging evaluation of acute ischemic stroke. J Int Med Res 2020; 48(1):300060518802530. DOI: https://doi.org/10.1177/0300060518802530

31. Soun JE, Chow DS, Nagamine M, Takhtawala RS, Filippi CG, Yu W, Chang PD. Artificial intelligence and acute stroke imaging. AJNR Am J Neuroradiol 2021; 42(1):2-11. DOI: https://doi.org/10.3174/ajnr.A6883

32. Mokli Y, Pfaff J, Dos Santos DP, Herweh C, Nagel S. Computer-aided imaging analysis in acute ischemic stroke - Background and clinical applications. Neurol Res Pract 2019; 1:23. DOI: https://doi.org/10.1186/s42466-019-0028-y

33. Dagonnier M, Donnan GA, Davis SM, Dewey HM, Howells DW. Acute stroke biomarkers: Are we there yet? Front Neurol 2021; 12:619721. DOI: https://doi.org/10.3389/fneur.2021.619721

34. Asadi H, Dowling R, Yan B, Mitchell P. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS One 2014; 9(2):e88225. DOI: https://doi.org/10.1371/journal.pone.0088225

35. Guo L, Abbosh A. Stroke localization and classification using microwave tomography with k-means clustering and support vector machine. Bioelectromagnetics 2018; 39(4):312-324. DOI: https://doi.org/10.1002/bem.22118

36. Cheng NT, Kim AS. Intravenous thrombolysis for acute ischemic stroke within 3 hours versus between 3 and 4.5 hours of symptom onset. Neurohospitalist 2015; 5(3):101-109. DOI: https://doi.org/10.1177/1941874415583116

37. Zhang J, Ta N, Fu M, Tian FH, Wang J, Zhang T, Wang B. Use of DWI-FLAIR mismatch to estimate the onset time in wake-up strokes. Neuropsychiatr Dis Treat 2022; 18:355-361. DOI: https://doi.org/10.2147/NDT.S351943

38. 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. Stroke 2020; 51(3):860-866. DOI: https://doi.org/10.1161/STROKEAHA.119.027611

39. Singh P, Kaur R, Kaur A. Endovascular treatment of acute ischemic stroke. J Neurosci Rural Pract 2013; 4(3):298-303. DOI: https://doi.org/10.4103/0976-3147.118787

40. Chen CJ, Ding D, Starke RM, Mehndiratta P, Crowley RW, Liu KC, Southerland AM, Worrall BB. Endovascular vs medical management of acute ischemic stroke. Neurology 2015; 85(22):1980-1990. DOI: https://doi.org/10.1212/WNL.0000000000002176

41. Hui W, Wu C, Zhao W, Sun H, Hao J, Liang H, Wang X, Li M, Jadhav AP, Han Y, Ji X. Efficacy and safety of recanalization therapy for acute ischemic stroke with large vessel occlusion: A systematic review. Stroke 2020; 51(7):2026-2035. DOI: https://doi.org/10.1161/STROKEAHA.119.028624

42. Arrarte Terreros N, Bruggeman AAE, Swijnenburg ISJ, van Meenen LCC, Groot AE, Coutinho JM, Roos YBWEM, Emmer BJ, Beenen LFM, van Bavel E, Marquering HA, Majoie CBLM. Early recanalization in large-vessel occlusion stroke patients transferred for endovascular treatment. J Neurointerv Surg 2022; 14(5):480-484. DOI: https://doi.org/10.1136/neurintsurg-2021-017441

43. Zelenak K, Krajina A, Meyer L, Fiehler J, Esmint Artificial Intelligence And Robotics Ad Hoc Committee, Behme D, Bulja D, Caroff J, Chotai AA, Da Ros V, Gentric JC, Hofmeister J, Kass-Hout O, Kocatürk O, Lynch J, Pearson E, Vukasinovic I. How to improve the management of acute ischemic stroke by modern technologies, artificial intelligence, and new treatment methods. Life (Basel) 2021; 11(6):488. DOI: https://doi.org/10.3390/life11060488

44. Czap AL, Bahr-Hosseini M, Singh N, Yamal JM, Nour M, Parker S, Kim Y, Restrepo L, Abdelkhaleq R, Salazar-Marioni S, Phan K, Bowry R, Rajan SS, Grotta JC, Saver JL, Giancardo L, Sheth SA. Machine learning automated detection of large vessel occlusion from mobile stroke unit computed tomography angiography. Stroke 2022; 53(5):1651-1656. DOI: https://doi.org/10.1161/STROKEAHA.121.036091

45. 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. Stroke 2019; 50(6):1444-1451. DOI: https://doi.org/10.1161/STROKEAHA.118.024261

46. Garg R, Oh E, Naidech A, Kording K, Prabhakaran S. Automating ischemic stroke subtype classification using machine learning and natural language processing. J Stroke Cerebrovasc Dis 2019; 28(7):2045-2051. DOI: https://doi.org/10.1016/j.jstrokecerebrovasdis.2019.02.004

47. Mainali S, Darsie ME, Smetana KS. Machine learning in action: Stroke diagnosis and outcome prediction. Front Neurol 2021; 12:734345. DOI: https://doi.org/10.3389/fneur.2021.734345

48. Kleindorfer DO, Towfighi A, Chaturvedi S, Cockroft KM, Gutierrez J, Lombardi-Hill D, Kamel H, Kernan WN, Kittner SJ, Leira EC, Lennon O, Meschia JF, Nguyen TN, Pollak PM, Santangeli P, Sharrief AZ, Smith SC Jr, Turan TN, Williams LS. 2021 Guideline for the prevention of stroke in patients with stroke and transient ischemic attack: A guideline from the American Heart Association/American Stroke Association Stroke 2021; 52(7):e364-e467. DOI: https://doi.org/10.1161/STR.0000000000000375. Erratum in: Stroke 2021; 52(7):e483-e484.

49. Schweitzer M, Hoerbst A. Robotic Assistance in Medication Management: Development and Evaluation of a Prototype. Stud Health Technol Inform 2016; 225:422-426.

50. Heit JJ, Iv M, Wintermark M. Imaging of intracranial hemorrhage. J Stroke 2017; 19(1):11-27. DOI: https://doi.org/10.5853/jos.2016.00563

51. Buchlak QD, Milne MR, Seah J, Johnson A, Samarasinghe G, Hachey B, Esmaili N, Tran A, Leveque JC, Farrokhi F, Goldschlager T, Edelstein S, Brotchie P. Charting the potential of brain computed tomography deep learning systems. J Clin Neurosci 2022; 99:217-223. DOI: https://doi.org/10.1016/j.jocn.2022.03.014

52. Burduja M, Ionescu RT, Verga N. Accurate and efficient intracranial hemorrhage detection and subtype classification in 3D CT scans with convolutional and long short-term memory neural networks. Sensors (Basel) 2020; 20(19):5611. DOI: https://doi.org/10.3390/s20195611

53. Wang T, Song N, Liu L, Zhu Z, Chen B, Yang W, Chen Z. Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement. BMC Med Imaging 2021; 21(1):125. DOI: https://doi.org/10.1186/s12880-021-00657-6

54. 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 intracerebral hemorrhage. Stroke 2020; 51(3):815-823. DOI: https://doi.org/10.1161/STROKEAHA.119.026764

55. Godoy DA, Pinero GR, Koller P, Masotti L, Di Napoli M. Steps to consider in the approach and management of critically ill patient with spontaneous intracerebral hemorrhage. World J Crit Care Med 2015; 4(3):213-229. DOI: https://doi.org/10.5492/wjccm.v4.i3.213

56. Vajsbaher T, Schultheis H, Francis NK. Spatial cognition in minimally invasive surgery: A systematic review. BMC Surg 2018; 18(1):94. DOI: https://doi.org/10.1186/s12893-018-0416-1

57. Wu S, Wang H, Wang J, Hu F, Jiang W, Lei T, Shu K. Effect of robot-assisted neuroendoscopic hematoma evacuation combined intracranial pressure monitoring for the treatment of hypertensive intracerebral hemorrhage. Front Neurol 2021; 12:722924. DOI: https://doi.org/10.3389/fneur.2021.722924

58. Donkor ES. Stroke in the 21st century: A snapshot of the burden, epidemiology, and quality of life. Stroke Res Treat 2018; 2018:3238165. DOI: https://doi.org/10.1155/2018/3238165

59. Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform 2020; 8(7):e18599. DOI: https://doi.org/10.2196/18599

60. Lowrey CR, Blazevski B, Marnet JL, Bretzke H, Dukelow SP, Scott SH. Robotic tests for position sense and movement discrimination in the upper limb reveal that they each are highly reproducible but not correlated in healthy individuals. J Neuroeng Rehabil 2020; 17(1):103. DOI: https://doi.org/10.1186/s12984-020-00721-2

61. 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. Arch Phys Med Rehabil 2019; 100(10):1907-1915. DOI: https://doi.org/10.1016/j.apmr.2019.03.016

62. Xu Y, Liu X, Cao X, Huang C, Liu E, Qian S, Liu X, Wu Y, Dong F, Qiu CW, Qiu J, Hua K, Su W, Wu J, Xu H, Han Y, Fu C, Yin Z, Liu M, Roepman R, Dietmann S, Virta M, Kengara F, Zhang Z, Zhang L, Zhao T, Dai J, Yang J, Lan L, Luo M, Liu Z, An T, Zhang B, He X, Cong S, Liu X, Zhang W, Lewis JP, Tiedje JM, Wang Q, An Z, Wang F, Zhang L, Huang T, Lu C, Cai Z, Wang F, Zhang J. Artificial intelligence: A powerful paradigm for scientific research. Innovation (Camb) 2021; 2(4):100179. DOI: https://doi.org/10.1016/j.xinn.2021.100179

63. Alonso-Valerdi LM, Luz María AV, Mercado-García VR, Víctor Rodrigo MG. Enrichment of human-computer interaction in brain-computer interfaces via virtual environments. Comput Intell Neurosci 2017; 2017:6076913. DOI: https://doi.org/10.1155/2017/6076913. Erratum in: Comput Intell Neurosci. 2018; 2018:7129735. Luz María AV [corrected to Alonso-Valerdi LM], Víctor Rodrigo MG [corrected to Mercado-García VR]

64. Mridha MF, Das SC, Kabir MM, Lima AA, Islam MR, Watanobe Y. Brain-computer interface: Advancement and challenges. Sensors (Basel) 2021; 21(17):5746. DOI: https://doi.org/10.3390/s21175746

65. Sebastián-Romagosa M, Cho W, Ortner R, Murovec N, Von Oertzen T, Kamada K, Allison BZ, Guger C. Brain computer interface treatment for motor rehabilitation of upper extremity of stroke patients-A feasibility study. Front Neurosci 2020; 14:591435. DOI: https://doi.org/10.3389/fnins.2020.591435

66. Hidler J, Sainburg R. Role of robotics in neurorehabilitation. Top Spinal Cord Inj Rehabil 2011; 17(1):42-49. DOI: https://doi.org/10.1310/sci1701-42

67. Kim WS, Cho S, Ku J, Kim Y, Lee K, Hwang HJ, Paik NJ. Clinical application of virtual reality for upper limb motor rehabilitation in stroke: Review of technologies and clinical evidence. J Clin Med 2020; 9(10):3369. DOI: https://doi.org/10.3390/jcm9103369

68. Mao Y, Chen P, Li L, Huang D. Virtual reality training improves balance function. Neural Regen Res 2014; 9(17):1628-1634. DOI: https://doi.org/10.4103/1673-5374.141795

69. Lv X, Chen H. Effect of virtual reality combined with intelligent exercise rehabilitation machine on the nursing recovery of lower limb motor function of patients with hypertensive stroke. J Healthc Eng 2022; 2022:2106836. DOI: https://doi.org/10.1155/2022/2106836
How to Cite
Garcia-Perez, M. (2022). Artificial Intelligence and Stroke Management. Science Insights, 40(6), 533–539. https://doi.org/10.15354/si.22.re059