Issue 5
Nov 2021
Turn off MathJax
Article Contents
JOURNAL OF MECHANICAL ENGINEERING  > 2021  >  12(5): 749-754.
HAN Xiaowei, LI Ming, ZHANG Bing. Application of Artificial Intelligence in Neuroimaging of Stroke[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 12(5): 749-754. doi: 10.12290/xhyxzz.2021-0491
Citation: HAN Xiaowei, LI Ming, ZHANG Bing. Application of Artificial Intelligence in Neuroimaging of Stroke[J]. JOURNAL OF MECHANICAL ENGINEERING, 2021, 12(5): 749-754. doi: 10.12290/xhyxzz.2021-0491
shu

Application of Artificial Intelligence in Neuroimaging of Stroke

doi: 10.12290/xhyxzz.2021-0491
Funds:

National Natural Science Foundation of China 81720108022

National Natural Science Foundation of China 81971596

National Natural Science Foundation of China 81701672

The Project of the "Sixth Peak of Talented People" WSN-138

Nanjing Medical Science and Technology Development Fund YKK16112

Key Medical Talents of the Jiangsu Province, the"13th Five-year"Health Promotion Project of the Jiangsu Province ZDRCA2016064

More Information
  • Corresponding author: ZHANG Bing  Tel: 86-25-83106666, E-mail: zhangbing_nanjing@nju.edu.cn
  • Received Date: 23 Jun 2021
  • Accepted Date: 29 Jul 2021
    • Available Online: 26 Nov 2021
  • Publish Date: 19 Aug 2021
  • Issue Publish Date: 30 Sep 2021
    Abstract
  • Artificial intelligence (AI) has risen rapidly in the research field of computer science. The amount of data generated in the process of medical imaging is huge, so it is very suitable to use artificial intelligence technology for related data processing. Neuroimaging of patients with stroke plays a key role in clinical diagnosis, treatment and follow-up. AI technology plays an increasingly important role in the processing and analysis of imaging data of stroke. This paper mainly reviews the research progress of AI technology in neuroimaging of ischemic and hemorrhagic stroke, focusing on the detection of ischemic stroke, judgment of ischemic state of responsible brain area and treatment evaluation, as well as the application of AI technology in the diagnosis, quantitative analysis and treatment evaluation of hemorrhagic stroke. At the same time, the current situation of its clinical transformation application was analyzed. Furthermore, it discusses the main limitations of the current application of AI in stroke neuroimaging, and prospects for future development.

     

    • FullText(HTML)
  • loading
    • References(44)
  • [1]
    Soun JE, Chow DS, Nagamine M, et al. Artificial Intelligence and Acute Stroke Imaging[J]. AJNR Am J Neuroradiol, 2021, 42: 2-11. doi: 10.3174/ajnr.A6883
    [2]
    Yedavalli VS, Tong E, Martin D, et al. Artificial intelli-gence in stroke imaging: current and future perspectives[J]. Clin Imaging, 2021, 69: 246-254. doi: 10.1016/j.clinimag.2020.09.005
    [3]
    Patel UK, Anwar A, Saleem S, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders[J]. J Neurol, 2021, 268: 1623-1642. doi: 10.1007/s00415-019-09518-3
    [4]
    Zhu G, Jiang B, Chen H, et al. Artificial Intelligence and Stroke Imaging: A West Coast Perspective[J]. Neuroimaging Clin N Am, 2020, 30: 479-492. doi: 10.1016/j.nic.2020.07.001
    [5]
    Herrgårdh T, Madai VI, Kelleher JD, et al. Hybrid modell-ing for stroke care: Review and suggestions of new approa-ches for risk assessment and simulation of scenarios[J]. Neuroimage Clin, 2021, 31: 102694. doi: 10.1016/j.nicl.2021.102694
    [6]
    Duong MT, Rauschecker AM, Mohan S. Diverse Applications of Artificial Intelligence in Neuroradiology[J]. Neuroimaging Clin N Am, 2020, 30: 505-516. doi: 10.1016/j.nic.2020.07.003
    [7]
    Kamal H, Lopez V, Sheth SA. Machine Learning in Acute Ischemic Stroke Neuroimaging[J]. Front Neurol, 2018, 9: 945. doi: 10.3389/fneur.2018.00945
    [8]
    Ho KC, Speier W, El-Saden S, et al. Classifying Acute Ischemic Stroke Onset Time using Deep Imaging Features[J]. AMIA Annu Symp Proc, 2017, 2017: 892-901. http://zerista.s3.amazonaws.com/item_files/3ea6/attachments/405195/original/101217_amia_presentation1.13_kch.pdf
    [9]
    Chen L, Bentley P, Rueckert D. Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks[J]. Neuroimage Clin, 2017, 15: 633-643. doi: 10.1016/j.nicl.2017.06.016
    [10]
    Garg R, Oh E, Naidech A, et al. Automating Ischemic Stroke Subtype Classification Using Machine Learning and Natural Language Processing[J]. J Stroke Cerebrovasc Dis, 2019, 28: 2045-2051. doi: 10.1016/j.jstrokecerebrovasdis.2019.02.004
    [11]
    Bouts MJ, Tiebosch IA, van der Toorn A, et al. Early Identification of Potentially Salvageable Tissue with MRI-Based Predictive Algorithms after Experimental Ischemic Stroke[J]. J Cereb Blood Flow Metab, 2013, 33: 1075-1082. doi: 10.1038/jcbfm.2013.51
    [12]
    Huang S, Shen Q, Duong TQ. Quantitative prediction of acute ischemic tissue fate using support vector machine[J]. Brain Res, 2011, 1405: 77-84. doi: 10.1016/j.brainres.2011.05.066
    [13]
    Rondina JM, Filippone M, Girolami M, et al. Decoding post-stroke motor function from structural brain imaging[J]. Neuroimage Clin, 2016, 12: 372-380. doi: 10.1016/j.nicl.2016.07.014
    [14]
    Forkert ND, Verleger T, Cheng B, et al. Multiclass Support Vector Machine-Based Lesion Mapping Predicts Functional Outcome in Ischemic Stroke Patients[J]. PloS One, 2015, 10: e129569. http://europepmc.org/articles/PMC4476759/pdf/pone.0129569.pdf
    [15]
    Tjoa E, Guan C. A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI[J]. IEEE Trans Neural Netw Learn Syst, 2020. doi: 10.1109/TNNLS.2020.3027314.
    [16]
    Zihni E, Madai VI, Livne M, et al. Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome[J]. PLoS One, 2020, 15: e231166. http://www.researchgate.net/publication/340468878_Opening_the_black_box_of_artificial_intelligence_for_clinical_decision_support_A_study_predicting_stroke_outcome
    [17]
    Bentley P, Ganesalingam J, Carlton Jones AL, et al. Prediction of stroke thrombolysis outcome using CT brain machine learning[J]. Neuroimage Clin, 2014, 4: 635-640. doi: 10.1016/j.nicl.2014.02.003
    [18]
    Scalzo F, Alger JR, Hu X, et al. Multi-center prediction of hemorrhagic transformation in acute ischemic stroke using permeability imaging features[J]. Magn Reson Imaging, 2013, 31: 961-969. doi: 10.1016/j.mri.2013.03.013
    [19]
    Zille M, Karuppagounder SS, Chen Y, et al. Neuronal Death After Hemorrhagic Stroke In Vitro and In Vivo Shares Features of Ferroptosis and Necroptosis[J]. Stroke, 2017, 48: 1033-1043. doi: 10.1161/STROKEAHA.116.015609
    [20]
    Ojaghihaghighi S, Vahdati SS, Mikaeilpour A, et al. Comparison of neurological clinical manifestation in patients with hemorrhagic and ischemic stroke[J]. World J Emerg Med, 2017, 8: 34-38. doi: 10.5847/wjem.j.1920-8642.2017.01.006
    [21]
    Xu J, Zhang R, Zhou Z, et al. Deep Network for the Automatic Segmentation and Quantification of Intracranial Hemorrhage on CT[J]. Front Neurosci, 2021, 14: 541817. doi: 10.3389/fnins.2020.541817
    [22]
    Zhao X, Chen K, Wu G, et al. Deep learning shows good reliability for automatic segmentation and volume measurement of brain hemorrhage, intraventricular extension, and peripheral edema[J]. Eur Radiol, 2021, 31: 5012-5020. doi: 10.1007/s00330-020-07558-2
    [23]
    Ironside N, Chen C, Mutasa S, et al. Fully Automated Segmentation Algorithm for Hematoma Volumetric Analysis in Spontaneous Intracerebral Hemorrhage[J]. Stroke, 2019, 50: 3416-3423. doi: 10.1161/STROKEAHA.119.026561
    [24]
    Cho J, Park K, Karki M, et al. Improving Sensitivity on Identification and Delineation of Intracranial Hemorrhage Lesion Using Cascaded Deep Learning Models[J]. J Digit Imaging, 2019, 32: 450-461. doi: 10.1007/s10278-018-00172-1
    [25]
    Kuo W, Häne C, Mukherjee P, et al. Expert-level detection of acute intracranial hemorrhage on head computed tomography using deep learning[J]. Proc Natl Acad Sci U S A, 2019, 116: 22737-22745. doi: 10.1073/pnas.1908021116
    [26]
    Karthik R, Menaka R, Johnson A, et al. Neuroimaging and deep learning for brain stroke detection - A review of recent advancements and future prospects[J]. Comput Methods Programs Biomed, 2020, 197: 105728. doi: 10.1016/j.cmpb.2020.105728
    [27]
    Yu Y, Guo D, Lou M, et al. Prediction of Hemorrhagic Transformation Severity in Acute Stroke From Source Perfusion MRI[J]. IEEE Trans Biomed Eng, 2018, 65: 2058-2065. doi: 10.1109/TBME.2017.2783241
    [28]
    Lin CH, Hsu KC, Johnson KR, et al. Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry[J]. Comput Methods Programs Biomed, 2020, 190: 105381. doi: 10.1016/j.cmpb.2020.105381
    [29]
    Çelik G, Baykan ÖK, Kara Y, et al. Predicting 10-day Mortality in Patients with Strokes Using Neural Networks and Multivariate Statistical Methods[J]. J Stroke Cerebrovasc Dis, 2014, 23: 1506-1512. doi: 10.1016/j.jstrokecerebrovasdis.2013.12.018
    [30]
    de Jong G, Aquarius R, Sanaan B, et al. Prediction Models in Aneurysmal Subarachnoid Hemorrhage: Forecasting Clinical Outcome With Artificial Intelligence[J]. Neurosurgery, 2021, 88: E427-E434. doi: 10.1093/neuros/nyaa581
    [31]
    Capoglu S, Savarraj JP, Sheth SA, et al. Representation Learning of 3D Brain Angiograms, an Application for Cerebral Vasospasm Prediction[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2019, 2019: 3394-3398. http://www.researchgate.net/publication/336336511_Representation_Learning_of_3D_Brain_Angiograms_an_Application_for_Cerebral_Vasospasm_Prediction
    [32]
    Ramos LA, van der Steen WE, Sales BR, et al. Machine learning improves prediction of delayed cerebral ischemia in patients with subarachnoid hemorrhage[J]. J Neurointerv Surg, 2019, 11: 497-502. doi: 10.1136/neurintsurg-2018-014258
    [33]
    Linder SM, Rosenfeldt AB, Reiss A, et al. The Home Stroke Rehabilitation and Monitoring System Trial: A Randomized Controlled Trial[J]. Inter J Stroke, 2013, 8: 46-53. doi: 10.1111/j.1747-4949.2012.00971.x
    [34]
    巫嘉陵. 当下有为未来可期: 人工智能与脑血管病[J]. 中国现代神经疾病杂志, 2021, 21: 1-2. https://www.cnki.com.cn/Article/CJFDTOTAL-XDJB202101003.htm
    [35]
    Fu F, Wei J, Zhang M, et al. Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network[J]. Nat Commun, 2020, 11: 4829. doi: 10.1038/s41467-020-18606-2
    [36]
    Teng L, Ren Q, Zhang P, et al. Artificial Intelligence Can Effectively Predict Early Hematoma Expansion of Intracerebral Hemorrhage Analyzing Noncontrast Computed Tomography Image[J]. Front Aging Neurosci, 2021, 13: 632138. doi: 10.3389/fnagi.2021.632138
    [37]
    Liu X, Feng J, Wu Z, et al. Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA[J]. Interv Neuroradiol, 2021. doi: 10.1177/15910199211000956.
    [38]
    Quispe-Orozco D, Farooqui M, Zevallos C, et al. Angiography Suite Cone-Beam Computed Tomography Perfusion Imaging in Large-Vessel Occlusion Patients Using RAPID Software: A Pilot Study[J]. Stroke, 2021. doi: 10.1161/STROKEAHA.121.035992.
    [39]
    Zhu G, Jiang B, Tong L, et al. Applications of Deep Learning to Neuro-Imaging Techniques[J]. Front Neurol, 2019, 10: 869. doi: 10.3389/fneur.2019.00869
    [40]
    Zaharchuk G, Gong E, Wintermark M, et al. Deep Learning in Neuroradiology[J]. AJNR Am J Neuroradiol, 2018, 39: 1776-1784. doi: 10.3174/ajnr.A5543
    [41]
    Pesapane F, Volonté C, Codari M, et al. Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States[J]. Insights Imaging, 2018, 9: 745-753. doi: 10.1007/s13244-018-0645-y
    [42]
    Tuladhar A, Gill S, Ismail Z, et al. Building machine learning models without sharing patient data: A simulation-based analysis of distributed learning by ensembling[J]. J Biomed Inform, 2020, 106: 103424. doi: 10.1016/j.jbi.2020.103424
    [43]
    Kaka H, Zhang E, Khan N. Artificial Intelligence and Deep Learning in Neuroradiology: Exploring the New Frontier[J]. Can Assoc Radiol J, 2021, 72: 35-44. doi: 10.1177/0846537120954293
    [44]
    Boyd C, Brown G, Kleinig T, et al. Machine Learning Quantitation of Cardiovascular and Cerebrovascular Disease: A Systematic Review of Clinical Applications[J]. Diagnostics (Basel), 2021, 11: 551. doi: 10.3390/diagnostics11030551
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views(195) PDF downloads(0) Cited by()
    Proportional views
    Related

    Copyright © 2009《 石油钻探技术 》 All Rights Reserved.

    Address：North-Star Times Tower, No.8 Beichendong Road, Chaoyang District, Beijing, China China Pos：100101

    Tel：010-84988317，84988356，84988357 Fax：021-64253812 Email：syzt@vip.163.com

    Beijing public network security equipment 11010502036328　Beijing ICP backup 17033152

    Supported by: Beijing Renhe Information Technology Co., Ltd.  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return