Artificial Intelligence and Stroke Management
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Published
May 30, 2022
Abstract
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.
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Keywords
Artificial Intelligence, Stroke Prevention, Neurorehabilitation
References
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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
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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
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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
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