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Emerging frontiers of artificial intelligence and machine learning in ischemic stroke: a comprehensive investigation of state-of-the-art methodologies, clinical applications, and unraveling challenges

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Abstract

At present, stroke remains the second highest cause of death globally and a leading cause of disability. From 1990 to 2019, the absolute number of strokes worldwide increased by 70.0%, and the prevalence of stroke increased by 85.0%, causing millions of deaths and disability. Ischemic stroke accounts for the majority of strokes, which is caused by arterial occlusion. Effective primary prevention strategies, early diagnosis, and timely interventions such as rapid reperfusion are in urgent implementation to control ischemic stroke. Otherwise, the stroke burden will probably continue to grow across the world as a result of population aging and an ongoing high prevalence of risk factors. To help with the diagnosis and management of ischemic stroke, newer techniques such as artificial intelligence (AI) are highly anticipated and may bring a new revolution. AI is a recent fast-growing research area which aims to mimic cognitive processes through a number of techniques such as machine learning (ML) methods of random forest learning (RFL) and convolutional neural networks (CNNs). With the help of AI, several momentous milestones have already been attained across diverse dimensions of ischemic stroke. In the context of predictive, preventive, and personalized medicine (PPPM/3PM), we aim to transform stroke care from a reactive to a proactive and individualized paradigm. In this way, AI demonstrates strong clinical utility across all three levels of prevention in ischemic stroke. In this paper, we synoptically illustrated the history and current situation of AI and ML. Then, we summarized their clinical applications and efficacy in the management of stroke. We finally provided an outlook on how AI approaches might contribute to enhancing favorable outcomes after stroke and proposed our suggestions on developing AI-based PPPM strategies.

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Data Availability

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Abbreviations

ADC:

apparent diffusion coefficient

AI:

artificial intelligence

AIS:

acute ischemic stroke

ANN:

artificial neural network

ATRIA:

anticoagulation and risk factors in atrial fibrillation

AUC:

area under curve

CNN:

convolutional neural network

CPU:

central processing unit

CSF:

cerebrospinal fluid

CTA:

CT angiography

CTP:

CT perfusion

DCNN:

deep convolutional neural network

DL:

deep learning

DRANet:

deep residual attention convolutional neural network

DWI:

diffusion weighted imaging

ECG:

electrocardiogram

END:

early neurological deterioration

FDA:

Food and Drug Administration

FLAIR:

fluid attenuated inversion recovery

HMCAS:

hyperdense middle cerebral artery sign

LASSO:

least absolute shrinkage and selection operator

LSTM:

long short-term memory

LVO:

large vessel occlusion

ML:

machine learning

MRA:

magnetic resonance angiography

MRI:

magnetic resonance imaging

MRS:

Modified Rankin Scale

MT:

mechanical thrombectomy

NCCT:

non-contrast CT

NIHSS:

National Institutes of Health Stroke Scale

pMCAO:

permanent middle cerebral artery occlusion

RCTs:

randomized controlled trials

SAP:

stroke-associated pneumonia

sICH:

symptomatic intracranial hemorrhage

SPAN:

stroke prognostication using age and NIHSS

Thrive:

totaled health risks in vascular events

TIA:

transient ischemic attack

tPA:

tissue plasminogen activator

VGG16:

Visual Geometry Group Network 16

vWF:

von Willebrand factor

WMH:

white matter hyperintensity

YOLOv3:

You Only Look Once v3

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. Lancet Neurol. 2019;18(5):439–58. https://doi.org/10.1016/S1474-4422(19)30034-1.

  2. 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. Lancet Neurol. 2021;20(10):795–820. https://doi.org/10.1016/S1474-4422(21)00252-0.

  3. Ajoolabady A, Wang S, Kroemer G, Penninger JM, Uversky VN, Pratico D, et al. Targeting autophagy in ischemic stroke: from molecular mechanisms to clinical therapeutics. Pharmacol Ther. 2021;225:107848.

    PubMed  PubMed Central  CAS  Google Scholar 

  4. GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736–88. https://doi.org/10.1016/S0140-6736(18)32203-7. Epub 2018 Nov 8. Erratum in: Lancet. 2019;393(10190):e44. Erratum in: Lancet. 2018;392(10160):2170.

  5. Feigin VL, Krishnamurthi RV, Parmar P, Norrving B, Mensah GA, Bennett DA, et al. Update on the Global Burden of Ischemic and Hemorrhagic Stroke in 1990-2013: the GBD 2013 study. Neuroepidemiology. 2015;45:161–76.

    PubMed  Google Scholar 

  6. Golubnitschaja O, Potuznik P, Polivka J, Pesta M, Kaverina O, Pieper CC, et al. Ischemic stroke of unclear aetiology: a case-by-case analysis and call for a multi-professional predictive, preventive and personalised approach. EPMA J. 2022;13:535–45.

    PubMed  PubMed Central  Google Scholar 

  7. DeLong JH, Ohashi SN, O'Connor KC, Sansing LH. Inflammatory responses after ischemic stroke. Semin Immunopathol. 2022;44:625–48.

    PubMed  Google Scholar 

  8. Datta A, Sarmah D, Mounica L, Kaur H, Kesharwani R, Verma G, et al. Cell death pathways in ischemic stroke and targeted pharmacotherapy. Transl Stroke Res. 2020;11:1185–202.

    PubMed  Google Scholar 

  9. Zheng Y, Guo Z, Zhang Y, Shang J, Yu L, Fu P, et al. Rapid triage for ischemic stroke: a machine learning-driven approach in the context of predictive, preventive and personalised medicine. EPMA J. 2022;13:285–98.

    PubMed  PubMed Central  Google Scholar 

  10. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25:44–56.

    PubMed  CAS  Google Scholar 

  11. Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69:127–57.

    PubMed  PubMed Central  Google Scholar 

  12. Heo J, Yoon JG, Park H, Kim YD, Nam HS, Heo JH. Machine learning-based model for prediction of outcomes in acute stroke. Stroke. 2019;50:1263–5.

    PubMed  Google Scholar 

  13. Yin T, Zheng H, Ma T, Tian X, Xu J, Li Y, et al. Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine. EPMA J. 2022;13:137–47.

    PubMed  PubMed Central  Google Scholar 

  14. Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69S:S36–40.

    PubMed  Google Scholar 

  15. Cornet G. Robot companions and ethics a pragmatic approach of ethical design. J Int Bioethique. 2013;24(49-58):179–80.

    Google Scholar 

  16. Chen M, Decary M. Artificial intelligence in healthcare: an essential guide for health leaders. Healthc Manage Forum. 2020;33:10–8.

    PubMed  Google Scholar 

  17. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2:230–43.

    PubMed  PubMed Central  Google Scholar 

  18. Ramesh AN, Kambhampati C, Monson JR, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86:334–8.

    PubMed  PubMed Central  CAS  Google Scholar 

  19. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28:73–81.

    PubMed  Google Scholar 

  20. Ravi D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2017;21:4–21.

    PubMed  Google Scholar 

  21. Sico JJ, Chang CC, So-Armah K, Justice AC, Hylek E, Skanderson M, et al. HIV status and the risk of ischemic stroke among men. Neurology. 2015;84:1933–40.

    PubMed  PubMed Central  Google Scholar 

  22. Maaijwee NA, Rutten-Jacobs LC, Schaapsmeerders P, van Dijk EJ, de Leeuw FE. Ischaemic stroke in young adults: risk factors and long-term consequences. Nat Rev Neurol. 2014;10:315–25.

    PubMed  Google Scholar 

  23. Vijayan M, Reddy PH. Stroke, vascular dementia, and Alzheimer's disease: molecular links. J Alzheimers Dis. 2016;54:427–43.

    PubMed  PubMed Central  Google Scholar 

  24. Huang D, Anguo L, Yue WS, Yin L, Tse HF, Siu CW. Refinement of ischemic stroke risk in patients with atrial fibrillation and CHA2 DS2 -VASc score of 1. Pacing Clin Electrophysiol. 2014;37:1442–7.

    PubMed  Google Scholar 

  25. Li W, Zeng X, Xu L, Wang T, Lin W, Li Y, et al. Optimized stratification of risk factors in and beyond the CHA2DS2-VASc score to differentiate the real thromboembolic risk in mainland China: a systematic review and meta-analysis. Ann Palliat Med. 2020;9:4252–61.

    PubMed  Google Scholar 

  26. Zhang AJ, Dhruv P, Choi P, Bakker C, Koffel J, Anderson D, et al. A systematic literature review of patients with carotid web and acute ischemic stroke. Stroke. 2018;49:2872–6.

    PubMed  Google Scholar 

  27. Singer DE, Chang Y, Borowsky LH, Fang MC, Pomernacki NK, Udaltsova N, et al. A new risk scheme to predict ischemic stroke and other thromboembolism in atrial fibrillation: the ATRIA study stroke risk score. J Am Heart Assoc. 2013;2:e000250.

    PubMed  PubMed Central  Google Scholar 

  28. Fox KAA, Lucas JE, Pieper KS, Bassand JP, Camm AJ, Fitzmaurice DA, et al. Improved risk stratification of patients with atrial fibrillation: an integrated GARFIELD-AF tool for the prediction of mortality, stroke and bleed in patients with and without anticoagulation. BMJ Open. 2017;7:e017157.

    PubMed  PubMed Central  Google Scholar 

  29. Hijazi Z, Lindbäck J, Alexander JH, Hanna M, Held C, Hylek EM, et al. The ABC (age, biomarkers, clinical history) stroke risk score: a biomarker-based risk score for predicting stroke in atrial fibrillation. Eur Heart J. 2016;37:1582–90.

    PubMed  PubMed Central  Google Scholar 

  30. You LR, Tang M. The association of high D-dimer level with high risk of ischemic stroke in nonvalvular atrial fibrillation patients: a retrospective study. Medicine (Baltimore). 2018;97:e12622.

    PubMed  PubMed Central  CAS  Google Scholar 

  31. Liu Y, Yin B, Cong Y. The probability of ischaemic stroke prediction with a multi-neural-network model. Sensors (Basel). 2020;20(17):4995. https://doi.org/10.3390/s20174995.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Li X, Bian D, Yu J, Mao H, Li M, Zhao D. Using machine learning models to classify stroke risk level based on national screening data. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:1386–90. https://doi.org/10.1109/EMBC.2019.8857657.

    Article  PubMed  Google Scholar 

  33. Levy BI, Ambrosio G, Pries AR, Struijker-Boudier HA. Microcirculation in hypertension: a new target for treatment? Circulation. 2001;104:735–40.

    PubMed  CAS  Google Scholar 

  34. Liew G, Wang JJ, Mitchell P, Wong TY. Retinal vascular imaging: a new tool in microvascular disease research. Circ Cardiovasc Imaging. 2008;1:156–61.

    PubMed  Google Scholar 

  35. De Silva DA, Manzano JJ, Liu EY, Woon FP, Wong WX, Chang HM, et al. Retinal microvascular changes and subsequent vascular events after ischemic stroke. Neurology. 2011;77:896–903.

    PubMed  Google Scholar 

  36. Rim TH, Teo AWJ, Yang HHS, Cheung CY, Wong TY. Retinal vascular signs and cerebrovascular diseases. J Neuroophthalmol. 2020;40:44–59.

    PubMed  Google Scholar 

  37. Coull AJ, Lovett JK, Rothwell PM. Population based study of early risk of stroke after transient ischaemic attack or minor stroke: implications for public education and organisation of services. Bmj. 2004;328:326.

    PubMed  PubMed Central  CAS  Google Scholar 

  38. Chan KL, Leng X, Zhang W, Dong W, Qiu Q, Yang J, et al. Early identification of high-risk TIA or minor stroke using artificial neural network. Front Neurol. 2019;10:171.

    PubMed  PubMed Central  Google Scholar 

  39. Chalela JA, Kidwell CS, Nentwich LM, Luby M, Butman JA, Demchuk AM, et al. Magnetic resonance imaging and computed tomography in emergency assessment of patients with suspected acute stroke: a prospective comparison. Lancet. 2007;369:293–8.

    PubMed  PubMed Central  Google Scholar 

  40. Akasaka T, Yakami M, Nishio M, Onoue K, Aoyama G, Nakagomi K, et al. Detection of suspected brain infarctions on CT can be significantly improved with temporal subtraction images. Eur Radiol. 2019;29:759–69.

    PubMed  Google Scholar 

  41. Winkler DT, Fluri F, Fuhr P, Wetzel SG, Lyrer PA, Ruegg S, et al. Thrombolysis in stroke mimics: frequency, clinical characteristics, and outcome. Stroke. 2009;40:1522–5.

    PubMed  Google Scholar 

  42. Herpich F, Rincon F. Management of acute ischemic stroke. Crit Care Med. 2020;48:1654–63.

    PubMed  PubMed Central  Google Scholar 

  43. Wang G, Song T, Dong Q, Cui M, Huang N, Zhang S. Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Med Image Anal. 2020;65:101787.

    PubMed  Google Scholar 

  44. Zhu H, Tong D, Zhang L, Wang S, Wu W, Tang H, et al. Temporally downsampled cerebral CT perfusion image restoration using deep residual learning. Int J Comput Assist Radiol Surg. 2020;15:193–201.

    PubMed  Google Scholar 

  45. Nazari-Farsani S, Nyman M, Karjalainen T, Bucci M, Isojarvi J, Nummenmaa L. Automated segmentation of acute stroke lesions using a data-driven anomaly detection on diffusion weighted MRI. J Neurosci Methods. 2020;333:108575.

    PubMed  Google Scholar 

  46. Nogueira RG, Jadhav AP, Haussen DC, Bonafe A, Budzik RF, Bhuva P, et al. Thrombectomy 6 to 24 hours after stroke with a mismatch between deficit and infarct. N Engl J Med. 2018;378:11–21.

    PubMed  Google Scholar 

  47. Albers GW, Marks MP, Kemp S, Christensen S, Tsai JP, Ortega-Gutierrez S, et al. Thrombectomy for stroke at 6 to 16 hours with selection by perfusion imaging. N Engl J Med. 2018;378:708–18.

    PubMed  PubMed Central  Google Scholar 

  48. Oman O, Makela T, Salli E, Savolainen S, Kangasniemi M. 3D convolutional neural networks applied to CT angiography in the detection of acute ischemic stroke. Eur Radiol Exp. 2019;3:8.

    PubMed  PubMed Central  Google Scholar 

  49. Kniep HC, Sporns PB, Broocks G, Kemmling A, Nawabi J, Rusche T, et al. Posterior circulation stroke: machine learning-based detection of early ischemic changes in acute non-contrast CT scans. J Neurol. 2020;267:2632–41.

    PubMed  PubMed Central  Google Scholar 

  50. Hsieh YZ, Luo YC, Pan C, Su MC, Chen CJ, Hsieh KL. Cerebral small vessel disease biomarkers detection on mri-sensor-based image and deep learning. Sensors (Basel). 2019;19(11):2573. https://doi.org/10.3390/s19112573.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Shinohara Y, Takahashi N, Lee Y, Ohmura T, Kinoshita T. Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke. Jpn J Radiol. 2020;38:112–7.

    PubMed  Google Scholar 

  52. Shinohara Y, Takahashi N, Lee Y, Ohmura T, Umetsu A, Kinoshita F, et al. Usefulness of deep learning-assisted identification of hyperdense MCA sign in acute ischemic stroke: comparison with readers' performance. Jpn J Radiol. 2020;38:870–7.

    PubMed  Google Scholar 

  53. Rudilosso S, Chui E, Stringer MS, Thrippleton M, Chappell F, Blair G, et al. Prevalence and significance of the vessel-cluster sign on susceptibility-weighted imaging in patients with severe small vessel disease. Neurology. 2022;99:e440–52.

    PubMed  PubMed Central  CAS  Google Scholar 

  54. Cannistraro RJ, Badi M, Eidelman BH, Dickson DW, Middlebrooks EH, Meschia JF. CNS small vessel disease: a clinical review. Neurology. 2019;92:1146–56.

    PubMed  PubMed Central  Google Scholar 

  55. Liu L, Kurgan L, Wu FX, Wang J. Attention convolutional neural network for accurate segmentation and quantification of lesions in ischemic stroke disease. Med Image Anal. 2020;65:101791.

    PubMed  Google Scholar 

  56. Lee H, Lee EJ, Ham S, Lee HB, Lee JS, Kwon SU, et al. Machine learning approach to identify stroke within 4.5 hours. Stroke. 2020;51:860–6.

    PubMed  Google Scholar 

  57. Rekik I, Allassonniere S, Carpenter TK, Wardlaw JM. Medical image analysis methods in MR/CT-imaged acute-subacute ischemic stroke lesion: segmentation, prediction and insights into dynamic evolution simulation models. A critical appraisal. Neuroimage Clin. 2012;1:164–78.

    PubMed  PubMed Central  Google Scholar 

  58. Clerigues A, Valverde S, Bernal J, Freixenet J, Oliver A, Llado X. Acute ischemic stroke lesion core segmentation in CT perfusion images using fully convolutional neural networks. Comput Biol Med. 2019;115:103487.

    PubMed  Google Scholar 

  59. Sheth SA, Lopez-Rivera V, Barman A, Grotta JC, Yoo AJ, Lee S, et al. Machine learning-enabled automated determination of acute ischemic core from computed tomography angiography. Stroke. 2019;50:3093–100.

    PubMed  Google Scholar 

  60. Sacco SE, Whisnant JP, Broderick JP, Phillips SJ, O'Fallon WM. Epidemiological characteristics of lacunar infarcts in a population. Stroke. 1991;22:1236–41.

    PubMed  CAS  Google Scholar 

  61. Broderick JP, Phillips SJ, O'Fallon WM, Frye RL, Whisnant JP. Relationship of cardiac disease to stroke occurrence, recurrence, and mortality. Stroke. 1992;23:1250–6.

    PubMed  CAS  Google Scholar 

  62. Kolominsky-Rabas PL, Weber M, Gefeller O, Neundoerfer B, Heuschmann PU. Epidemiology of ischemic stroke subtypes according to TOAST criteria: incidence, recurrence, and long-term survival in ischemic stroke subtypes: a population-based study. Stroke. 2001;32:2735–40.

    PubMed  CAS  Google Scholar 

  63. Sacco RL, Foulkes MA, Mohr JP, Wolf PA, Hier DB, Price TR. Determinants of early recurrence of cerebral infarction. The Stroke Data Bank. Stroke. 1989;20:983–9.

    PubMed  CAS  Google Scholar 

  64. Petty GW, Brown RD Jr, Whisnant JP, Sicks JD, O’Fallon WM, Wiebers DO. Ischemic stroke subtypes : a population-based study of functional outcome, survival, and recurrence. Stroke. 2000;31:1062–8.

    PubMed  CAS  Google Scholar 

  65. Lovett JK, Coull AJ, Rothwell PM. Early risk of recurrence by subtype of ischemic stroke in population-based incidence studies. Neurology. 2004;62:569–73.

    PubMed  CAS  Google Scholar 

  66. Risk factors for stroke and efficacy of antithrombotic therapy in atrial fibrillation. Analysis of pooled data from five randomized controlled trials. Arch Intern Med. 1994;154(13):1449-57. Erratum in: Arch Intern Med 1994;154(19):2254.

  67. Rothwell PM, Eliasziw M, Gutnikov SA, Fox AJ, Taylor DW, Mayberg MR, et al. Analysis of pooled data from the randomised controlled trials of endarterectomy for symptomatic carotid stenosis. Lancet (London, England). 2003;361:107–16.

    PubMed  CAS  Google Scholar 

  68. Rutten-Jacobs LC, Maaijwee NA, Arntz RM, Schoonderwaldt HC, Dorresteijn LD, van der Vlugt MJ, et al. Long-term risk of recurrent vascular events after young stroke: the FUTURE study. Ann Neurol. 2013;74:592–601.

    PubMed  Google Scholar 

  69. Rutten-Jacobs LC, Arntz RM, Maaijwee NA, Schoonderwaldt HC, Dorresteijn LD, van Dijk EJ, et al. Long-term mortality after stroke among adults aged 18 to 50 years. Jama. 2013;309:1136–44.

    PubMed  CAS  Google Scholar 

  70. Redfors P, Jood K, Holmegaard L, Rosengren A, Blomstrand C, Jern C. Stroke subtype predicts outcome in young and middle-aged stroke sufferers. Acta Neurol Scand. 2012;126:329–35.

    PubMed  CAS  Google Scholar 

  71. Singhal AB, Biller J, Elkind MS, Fullerton HJ, Jauch EC, Kittner SJ, et al. Recognition and management of stroke in young adults and adolescents. Neurology. 2013;81:1089–97.

    PubMed  PubMed Central  CAS  Google Scholar 

  72. Adams HP Jr, Bendixen BH, Kappelle LJ, Biller J, Love BB, Gordon DL, et al. Classification of subtype of acute ischemic stroke. Definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke. 1993;24:35–41.

    PubMed  Google Scholar 

  73. 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;28:2045–51.

    PubMed  Google Scholar 

  74. Nishio M, Koyasu S, Noguchi S, Kiguchi T, Nakatsu K, Akasaka T, et al. Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model. Comput Methods Prog Biomed. 2020;196:105711.

    Google Scholar 

  75. Rava RA, Peterson BA, Seymour SE, Snyder KV, Mokin M, Waqas M, et al. Validation of an artificial intelligence-driven large vessel occlusion detection algorithm for acute ischemic stroke patients. Neuroradiol J. 2021;34:408–17.

    PubMed  PubMed Central  Google Scholar 

  76. National Institute of Neurological Disorders and Stroke rt-PA Stroke Study Group. Tissue plasminogen activator for acute ischemic stroke. N Engl J Med. 1995;333(24):1581–7. https://doi.org/10.1056/NEJM199512143332401.

  77. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. Guidelines for the Early Management of Patients With Acute Ischemic Stroke: 2019 Update to the 2018 Guidelines for the Early Management of Acute Ischemic Stroke: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke. 2019;50:e344–418.

    PubMed  Google Scholar 

  78. Powers WJ. Acute ischemic stroke. N Engl J Med. 2020;383:252–60.

    PubMed  Google Scholar 

  79. Albers GW, Thijs VN, Wechsler L, Kemp S, Schlaug G, Skalabrin E, et al. Magnetic resonance imaging profiles predict clinical response to early reperfusion: the diffusion and perfusion imaging evaluation for understanding stroke evolution (DEFUSE) study. Ann Neurol. 2006;60:508–17.

    PubMed  Google Scholar 

  80. Fan Y, Chen Z, Zhang M. Role of exosomes in the pathogenesis, diagnosis, and treatment of central nervous system diseases. J Transl Med. 2022;20:291.

    PubMed  PubMed Central  CAS  Google Scholar 

  81. Fan Y, Lv X, Chen Z, Peng Y, Zhang M. m6A methylation: critical roles in aging and neurological diseases. Front Mol Neurosci. 2023;16:1102147.

    PubMed  PubMed Central  CAS  Google Scholar 

  82. 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. IEEE Trans Med Imaging. 2019;38(7):1666–76. https://doi.org/10.1109/TMI.2019.2901445.

    Article  PubMed  PubMed Central  Google Scholar 

  83. Phipps MS, Cronin CA. Management of acute ischemic stroke. BMJ. 2020;368:l6983.

    PubMed  Google Scholar 

  84. Yu Y, Xie Y, Thamm T, Gong E, Ouyang J, Huang C, et al. Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging. JAMA Netw Open. 2020;3:e200772.

    PubMed  PubMed Central  Google Scholar 

  85. Campbell BC, Mitchell PJ, Kleinig TJ, Dewey HM, Churilov L, Yassi N, et al. Endovascular therapy for ischemic stroke with perfusion-imaging selection. N Engl J Med. 2015;372:1009–18.

    PubMed  CAS  Google Scholar 

  86. Kuo DP, Kuo PC, Chen YC, Kao YJ, Lee CY, Chung HW, et al. Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model. J Biomed Sci. 2020;27:80.

    PubMed  PubMed Central  CAS  Google Scholar 

  87. Kumar A, Upadhyay N, Ghosal P, Chowdhury T, Das D, Mukherjee A, et al. CSNet: A new DeepNet framework for ischemic stroke lesion segmentation. Comput Methods Prog Biomed. 2020;193:105524.

    Google Scholar 

  88. Gupta A, Vupputuri A, Ghosh N. Delineation of ischemic core and penumbra volumes from MRI using MSNet Architecture. Annu Int Conf IEEE Eng Med Biol Soc. 2019;2019:6730–3.

    PubMed  Google Scholar 

  89. Maas MB, Furie KL, Lev MH, Ay H, Singhal AB, Greer DM, et al. National Institutes of Health Stroke Scale score is poorly predictive of proximal occlusion in acute cerebral ischemia. Stroke. 2009;40:2988–93.

    PubMed  PubMed Central  Google Scholar 

  90. Thomas S, de la Pena P, Butler L, Akbilgic O, Heiferman DM, Garg R, et al. Machine learning models improve prediction of large vessel occlusion and mechanical thrombectomy candidacy in acute ischemic stroke. J Clin Neurosci. 2021;91:383–90.

    PubMed  PubMed Central  Google Scholar 

  91. Ropper AH. Lateral displacement of the brain and level of consciousness in patients with an acute hemispheral mass. N Engl J Med. 1986;314:953–8.

    PubMed  CAS  Google Scholar 

  92. Qureshi AI, Suarez JI, Yahia AM, Mohammad Y, Uzun G, Suri MF, et al. Timing of neurologic deterioration in massive middle cerebral artery infarction: a multicenter review. Crit Care Med. 2003;31:272–7.

    PubMed  Google Scholar 

  93. Dhar R. Automated quantitative assessment of cerebral edema after ischemic stroke using CSF volumetrics. Neurosci Lett. 2020;724:134879.

    PubMed  PubMed Central  CAS  Google Scholar 

  94. Chung CC, Chan L, Bamodu OA, Hong CT, Chiu HW. Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death. Sci Rep. 2020;10:20501.

    PubMed  PubMed Central  CAS  Google Scholar 

  95. Finlayson O, Kapral M, Hall R, Asllani E, Selchen D, Saposnik G. Risk factors, inpatient care, and outcomes of pneumonia after ischemic stroke. Neurology. 2011;77:1338–45.

    PubMed  CAS  Google Scholar 

  96. Li X, Wu M, Sun C, Zhao Z, Wang F, Zheng X, et al. Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients. Eur J Neurol. 2020;27:1656–63.

    PubMed  CAS  Google Scholar 

  97. Rathore SS, Hinn AR, Cooper LS, Tyroler HA, Rosamond WD. Characterization of incident stroke signs and symptoms: findings from the atherosclerosis risk in communities study. Stroke. 2002;33:2718–21.

    PubMed  Google Scholar 

  98. Slujitoru AS, Enache AL, Pintea IL, Rolea E, Stocheci CM, Pop OT, et al. Clinical and morphological correlations in acute ischemic stroke. Romanian J Morphol Embryol. 2012;53:917–26.

    Google Scholar 

  99. Heo TS, Kim YS, Choi JM, Jeong YS, Seo SY, Lee JH, Jeon JP, Kim C. Prediction of stroke outcome using natural language processing-based machine learning of radiology report of brain MRI. J Pers Med. 2020;10(4):286. https://doi.org/10.3390/jpm10040286.

    Article  PubMed  PubMed Central  Google Scholar 

  100. Kogan E, Twyman K, Heap J, Milentijevic D, Lin JH, Alberts M. Assessing stroke severity using electronic health record data: a machine learning approach. BMC Med Inform Decis Mak. 2020;20:8.

    PubMed  PubMed Central  Google Scholar 

  101. Sung SM, Kang YJ, Cho HJ, Kim NR, Lee SM, Choi BK, et al. Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms. Clin Neurol Neurosurg. 2020;195:105892.

    PubMed  Google Scholar 

  102. Knoflach M, Matosevic B, Rücker M, Furtner M, Mair A, Wille G, et al. Functional recovery after ischemic stroke--a matter of age: data from the Austrian Stroke Unit Registry. Neurology. 2012;78:279–85.

    PubMed  CAS  Google Scholar 

  103. Nishi H, Oishi N, Ishii A, Ono I, Ogura T, Sunohara T, et al. Predicting clinical outcomes of large vessel occlusion before mechanical thrombectomy using machine learning. Stroke. 2019;50:2379–88.

    PubMed  Google Scholar 

  104. 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. Front Neurol. 2020;11:889. https://doi.org/10.3389/fneur.2020.00889.

    Article  PubMed  PubMed Central  Google Scholar 

  105. Chung CC, Hong CT, Huang YH, Su EC, Chan L, Hu CJ, et al. Predicting major neurologic improvement and long-term outcome after thrombolysis using artificial neural networks. J Neurol Sci. 2020;410:116667.

    PubMed  CAS  Google Scholar 

  106. Liebeskind DS, Tomsick TA, Foster LD, Yeatts SD, Carrozzella J, Demchuk AM, et al. Collaterals at angiography and outcomes in the Interventional Management of Stroke (IMS) III trial. Stroke. 2014;45:759–64.

    PubMed  PubMed Central  CAS  Google Scholar 

  107. Goyal M, Demchuk AM, Menon BK, Eesa M, Rempel JL, Thornton J, et al. Randomized assessment of rapid endovascular treatment of ischemic stroke. N Engl J Med. 2015;372:1019–30.

    PubMed  CAS  Google Scholar 

  108. Bang OY, Saver JL, Buck BH, Alger JR, Starkman S, Ovbiagele B, et al. Impact of collateral flow on tissue fate in acute ischaemic stroke. J Neurol Neurosurg Psych. 2008;79:625–9.

    CAS  Google Scholar 

  109. Seker F, Potreck A, Möhlenbruch M, Bendszus M, Pham M. Comparison of four different collateral scores in acute ischemic stroke by CT angiography. J Neurointerv Surg. 2016;8:1116–8.

    PubMed  Google Scholar 

  110. Su J, Li S, Wolff L, van Zwam W, Niessen WJ, van der Lugt A, et al. Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images. Med Image Anal. 2023;84:102724.

    PubMed  Google Scholar 

  111. Su J, Wolff L, van Es ACGM, van Zwam W, CWJ M, Dippel D, et al. Automatic collateral scoring from 3D CTA images. IEEE Trans Med Imaging. 2020;39:2190–200.

    PubMed  Google Scholar 

  112. To M N N, Kim HJ, Roh HG, Cho YS, Kwak JT. Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke. Int J Comput Assist Radiol Surg. 2020;15:151–62.

    PubMed  Google Scholar 

  113. Househ MS, Aldosari B, Alanazi A, Kushniruk AW, Borycki EM. Big data, big problems: a healthcare perspective. Stud Health Technol Inform. 2017;238:36–9.

    PubMed  Google Scholar 

  114. Torres Crigna A, Link B, Samec M, Giordano FA, Kubatka P, Golubnitschaja O. Endothelin-1 axes in the framework of predictive, preventive and personalised (3P) medicine. EPMA J. 2021;12:265–305.

    PubMed  PubMed Central  Google Scholar 

  115. Koklesova L, Mazurakova A, Samec M, Biringer K, Samuel SM, Büsselberg D, et al. Homocysteine metabolism as the target for predictive medical approach, disease prevention, prognosis, and treatments tailored to the person. EPMA J. 2021;12:477–505.

    PubMed  PubMed Central  Google Scholar 

  116. Koklesova L, Mazurakova A, Samec M, Kudela E, Biringer K, Kubatka P, et al. Mitochondrial health quality control: measurements and interpretation in the framework of predictive, preventive, and personalized medicine. EPMA J. 2022;13:177–93.

    PubMed  PubMed Central  Google Scholar 

  117. Evsevieva M, Sergeeva O, Mazurakova A, Koklesova L, Prokhorenko-Kolomoytseva I, Shchetinin E, et al. Pre-pregnancy check-up of maternal vascular status and associated phenotype is crucial for the health of mother and offspring. EPMA J. 2022;13:351–66.

    PubMed  PubMed Central  Google Scholar 

  118. Kropp M, Golubnitschaja O, Mazurakova A, Koklesova L, Sargheini N, Vo T-TKS, et al. Diabetic retinopathy as the leading cause of blindness and early predictor of cascading complications-risks and mitigation. EPMA J. 2023;14:21–42.

    PubMed  PubMed Central  Google Scholar 

  119. Kropp M, De Clerck E, Vo T-TKS, Thumann G, Costigliola V, Golubnitschaja O. Short communication: unique metabolic signature of proliferative retinopathy in the tear fluid of diabetic patients with comorbidities - preliminary data for PPPM validation. EPMA J. 2023;14:43–51.

    PubMed  PubMed Central  Google Scholar 

  120. Crigna AT, Samec M, Koklesova L, Liskova A, Giordano FA, Kubatka P, et al. Cell-free nucleic acid patterns in disease prediction and monitoring-hype or hope? EPMA J. 2020;11:603–27.

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We would like to show our deepest gratitude to all persons who have made substantial contributions to the work reported in the manuscript.

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This research was funded by the Natural Science Foundations for Excellent Young Scholars of Hunan Province (No.2021JJ20095), the Key Research and Development Program of Hunan Province (No. 2020SK2063), Research Project on Education and Teaching Innovation of Central South University (2021jy145), the Natural Science Foundations of Hunan Province (No.2020JJ4134), and the National Natural Science Foundation of China (No. 81501025).

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  1. Department of Neurology, Xiangya Hospital, Central South University, Changsha, 410008, China

    Yishu Fan, Zhenshan Song & Mengqi Zhang

  2. National Clinical Research Center for Geriatric Disorders，Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, 410008, Hunan, China

    Yishu Fan & Mengqi Zhang

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  1. Yishu Fan
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MZ conceptualized the study, acquired funding, and administered the project. YF and ZS wrote the original draft. YF and MZ reviewed and edited the manuscript.

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Correspondence to Mengqi Zhang.

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Fan, Y., Song, Z. & Zhang, M. Emerging frontiers of artificial intelligence and machine learning in ischemic stroke: a comprehensive investigation of state-of-the-art methodologies, clinical applications, and unraveling challenges. EPMA Journal 14, 645–661 (2023). https://doi.org/10.1007/s13167-023-00343-3

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