Assessment of CT for the categorization of hemorrhagic stroke (HS) and cerebral amyloid angiopathy hemorrhage (CAAH): A review

• Autorzy: Sudarshan Vidya K. , Raghavendra U. , Gudigar Anjan , Ciaccio Edward J. , Vijayananthan Anushya , Sahathevan Ramesh , Acharya U. Rajendra

Identyfikatory

DOI

10.1016/j.bbe.2022.07.001

• Języki publikacji: EN

Abstrakty

EN

The management of intracerebral hemorrhage (ICH) requires prompt diagnostic assessment and recognition. Accurate localization and categorization of ICH-type is crucial. There are two main categories of ICH: 1) hemorrhagic stroke (HS), which occurs in the deeper or subcortical regions of the brain, where the arterial network tapers to fine end-arteries, and, 2) cerebral amyloid angiopathy hemorrhage (CAAH), which occurs at the superficial or cortical-subcortical region of the grey and white matter junction. Computed tomography (CT) and magnetic resonance imaging (MRI) are the most used imaging tools in diagnosing ICH. However, availability, time, and cost often prevent emergent MRI use. Therefore, CT remains the primary tool in the diagnosis of ICH. The assessment of imaging studies is time-dependent, and a radiologist should do a detailed diagnostic evaluation. Human error can occur in a pressured clinical setting, even for highly trained medical professionals. Assisted or automated computer-aided analysis of CT/MRI may help to reduce the assessment time, improve the diagnostic accuracy, better differentiate between types of ICH, and reduce the risk of human errors. This review evaluates CT and MRI’s role in distinguishing between the two varieties of ICH-HS and CAAH. It focuses on how CT could be utilized as the preferred diagnostic tool. In addition, we discuss the role of automation using machine learning (ML) and the role or advantages of ML in the automated assessment of CT for the detection and classification of HS and CAAH. We have included our observations for future research and the requirements for further evaluation.

Słowa kluczowe

EN

intracerebral hemorrhage; ICH; hemorrhagic stroke; computed tomography; machine learning

PL

krwotok śródmózgowy; udar krwotoczny; tomografia komputerowa; uczenie maszynowe

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 3
• Strony: 888--901
• Opis fizyczny: Bibliogr. 113 poz., rys., tab.

Twórcy

autor

Sudarshan Vidya K.

• School of Computer Science and Engineering, Nanyang Technological University, NTU, Singapore

autor

Raghavendra U.

• Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India

autor

Gudigar Anjan

• Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, India

autor

Ciaccio Edward J.

• Department of Medicine - Cardiology, Columbia University, New York, NY, USA

autor

Vijayananthan Anushya

• Department of Biomedical Imaging, University of Malaya Medical Centre, Kuala Lumpur, Malaysia
• University of Malaya Research Imaging Centre, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia

autor

Sahathevan Ramesh

• Department of General Medicine, Ballarat Health Services, Ballarat, Australia

autor

Acharya U. Rajendra

• School of Engineering, Ngee Ann Polytechnic, Clementi, Singapore
• School of Science and Technology, Singapore University of Social Sciences, Singapore
• Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
• International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan

Bibliografia

• [1] Tadi P, Lui F, Stroke A. NCBI Bookshelf. A service of the National Library of Medicine. National Institutes of Health; 2021.
• [2] Feigin VL, Lawes CMM, Bennett DA, Barker-Collo SL, Parag V. Worldwide stroke incidence and early case fatality reported in 56 population-based studies: a systematic review. Lancet Neurol 2009;8(4):355–69.
• [3] Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart Disease and Stroke Statistics—2021 Update: A Report From the American Heart Association. Circulation 2021;143(8).
• [4] Samarasekera N, Smith C, Al-Shahi Salman R. The association between cerebral amyloid angiopathy and intracerebral haemorrhage: systematic review and meta-analysis. J Neurol Neurosurg Psychiatry 2012;83(3):275–81.
• [5] Hammerbeck U, Abdulle A, Heal C, Parry-Jones AR. Hyperacute prediction of functional outcome in spontaneous intracerebral haemorrhage: systematic review and meta-analysis. Eur Stroke J 2022;7(1):6–14.
• [6] Sacco S, Marini C, Toni D, Olivieri L, Carolei A. Incidence and 10-year survival of intracerebral hemorrhage in a population-based registry. Stroke 2009;40(2):394–9.
• [7] Donkor ES. Stroke in the 21st century: A snapshot of the burden, epidemiology, and quality of life. Stroke Res Treat 2018;2018:3238165.
• [8] O’Donnell MJ, Xavier D, Liu L, Zhang H, Chin SL, Rao-Melacini P, et al. Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. The Lancet 2010;376(9735):112–23.
• [9] Aronowski J, Zhao X. Molecular pathophysiology of cerebral hemorrhage: Secondary brain injury. Stroke 2011;42(6):1781–6.
• [10] Shi Y, Wardlaw JM. Update on cerebral small vessel disease: a dynamic whole-brain disease. Stroke Vasc Neurol 2016;1(3):83–92.
• [11] Charidimou A, Gang Q, Werring DJ. Sporadic cerebral amyloid angiopathy revisited: recent insights into pathophysiology and clinical spectrum. J Neurol Neurosurg Psychiatry 2012;83(2):124–37.
• [12] Elliott J, Smith M. The acute management of intracerebral hemorrhage: a clinical review. AnesthAnalg 2010;110(5):1419–27.
• [13] Flaherty ML, Woo D, Haverbusch M, Sekar P, Khoury J, Sauerbeck L, et al. Racial variations in location and risk of intracerebral hemorrhage. Stroke 2005;36(5):934–7.
• [14] Rosand J, Hylek EM, O’Donnell HC, Greenberg SM. Warfarin-associated hemorrhage and cerebral amyloid angiopathy: a genetic and pathologic study. Neurology 2000;55(7):947–51.
• [15] Pantoni L. Cerebral small vessel disease: from pathogenesis and clinical characteristics to therapeutic challenges. Lancet Neurol 2010;9(7):689–701.
• [16] Hanley DF, Awad IA, Vespa PM, Martin NA, Zuccarello M. Hemorrhagic stroke: introduction. Stroke 2013;44(6 Suppl 1): S65–6.
• [17] Yamada M. Cerebral amyloid angiopathy: emerging concepts. J Stroke 2015;17:17–30.
• [18] Chao CP, Kotsenas AL, Broderick DF. Cerebral amyloid angiopathy: CT and MR imaging finding. Radiographics 2006;26(5):1517–31.
• [19] Biffi A, Halpin A, Towfighi A, Gilson A, Busl K, Rost N, et al. Aspirin and recurrent intracerebral hemorrhage in cerebral amyloid angiopathy. Neurology 2010;75(8):693–8.
• [20] Linn J, Halpin A, Demaerel P, Ruhland J, Giese AD, Dichgans M, et al. Prevalence of superficial siderosis in patients with cerebral amyloid angiopathy. Neurology 2010;74(17):1346–50.
• [21] Hemphill JC, Greenberg SM, Anderson CS, Becker K, Bendok BR, Cushman M, et al. Guidelines for the management of spontaneous intracerebral hemorrhage: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2015;46(7):2032–60.
• [22] 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(9558):293–8.
• [23] Brazzelli M, Ag Sandercock P, Chappell FM, et al. Magnetic resonance imaging versus computed tomography for detection of acute vascular lesions in patients presenting with stroke symptoms. Cochrane Database Sys Rev 2009.
• [24] Delcourt C, Zhang S, Arima H, Sato S, Salman R-S, Wang X, et al. Significance of hematoma shape and density in intracerebral hemorrhage, the intensive blood pressure reduction in acute intracerebral hemorrhage trial study. Stroke 2016;47(5):1227–32.
• [25] Gregoire SM, Charidimou A, Gadapa N, Dolan E, Antoun N, Peeters A, et al. Acute ischaemic brain lesions in intracerebral haemorrhage: multicentre cross-sectional magnetic resonance imaging study. Brain 2011;134:2376–86.
• [26] Wada R, Aviv RI, Fox AJ, Sahlas DJ, Gladstone DJ, Tomlinson G, et al. CT angiography ‘‘spot sign” predicts hematoma expansion in acute intracerebral hemorrhage. Stroke 2007;38(4):1257–62.
• [27] Samarasekera N, Rodrigues MA, Toh PS, Salman R-S, Ai J. Imaging features of intracerebral hemorrhage with cerebral amyloid angiopathy: systematic review and meta-analysis. PLoS ONE 2017;12(7):e0180923.
• [28] Mirza S, Gokhale S. Neuroimaging in Intracerebral Hemorrhage. In: Agrawal A, editor. Hemorrhagic Stroke - An Update. InTech; 2017.
• [29] Bradley Jr WG. MR appearance of hemorrhage in the brain. Radiology 1993 Oct;189(1):15–26.
• [30] Knudsen KA, Rosand J, Karluk D, Greenberg SM. Clinical diagnosis of cerebral amyloid angiopathy: validation of the Boston criteria. Neurology 2001;56(4):537–9.
• [31] Linn J. Imaging of cerebral microbleeds. Clin Neuroradiol 2015;25(Suppl 2):167–75.
• [32] Roob G, Lechner A, Schmidt R, Flooh E, Hartung HP, Fazekas F. Frequency and location of microbleeds in patients with primary intracerebral hemorrhage. Stroke 2000;31(11):2665–9.
• [33] Macellari F, Paciaroni M, Agnelli G, Caso V. Neuroimaging in intracerebral hemorrhage. Stroke 2014;45(3):903–8.
• [34] Rodrigues MA, Samarasekera N, Lerpiniere C, Humphreys C, McCarron MO, White PM, et al. The Edinburgh CT and genetic diagnostic criteria for lobar intracerebral haemorrhage associated with cerebral amyloid angiopathy: model development and diagnostic test accuracy study. Lancet Neurol 2018;17(3):232–40.
• [35] Hussein HM, Tariq NA, Palesch YY, Qureshi AI. Reliability of hematoma volume measurement at local sites in a multicenter acute intracerebral hemorrhage clinical trial. Stroke 2013;44(1):237–9.
• [36] Webb AJS, Ullman NL, Morgan TC, Muschelli J, Kornbluth J, Awad IA, et al. Accuracy of the ABC/2 score for intracerebral hemorrhage: Systematic review and analysis of MISTIE, CLEAR-IVH. CLEAR III Stroke 2015;46(9):2470–6.
• [37] Huttner HB, Steiner T, Hartmann M, Köhrmann M, Juettler E, Mueller S, et al. Comparison of ABC/2 estimation technique to computer-assisted planimetric analysis in warfarin-related intracerebral parenchymal hemorrhage. Stroke 2006;37(2):404–8.
• [38] Wu TY et al. Software output from semi-automated planimetry can underestimate intracerebral haemorrhage and peri-haematomal oedema volumes by up to 41%. Neuroradiology 2016;58:867–76.
• [39] Divani AA, Majidi S, Luo X, Souslian FG, Zhang J, Abosch A, et al. The ABCs of accurate volumetric measurement of cerebral hematoma. Stroke 2011 Jun;42(6):1569–74.
• [40] Wang T, Song Na, Liu L, Zhu Z, Chen B, Yang W, et al. Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement. BMC Med Imaging 2021;21(1).
• [41] Rava RA, Seymour SE, LaQue ME, Peterson BA, Snyder KV, Mokin M, et al. Assessment of an artificial intelligence algorithm for detection of intracranial hemorrhage. World Neurosurg 2021 Jun;150:e209–17.
• [42] Zeleňák K, Krajina A, Meyer L, Fiehler J, ESMINT Artificial Intelligence and Robotics Ad hoc Committee, Behme D, et al. How to improve the management of acute ischemic stroke by modern technologies. Artif Intell New Treatment MethodsLife 2021;11:488.
• [43] Nawabi J, Kniep H, Elsayed S, Friedrich C, Sporns P, Rusche T, et al. Imaging-based outcome prediction of acute intracerebral hemorrhage. Transl Stroke Res 2021;12(6):958–67.
• [44] Gruschwitz P, Grunz J-P, Kuhl PJ, Kosmala A, Bley TA, Petritsch B, et al. Performance testing of a novel deep learning algorithm for the detection of intracranial hemorrhage and first trial under clinical conditions. Neurosci Inf 2021;1(1-2):100005.
• [45] Ye H, Gao F, Yin Y, Guo D, Zhao P, Lu Yi, et al. Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network. Eur Radiol 2019;29(11):6191–201.
• [46] Arbabshirani MR, Fornwalt BK, Mongelluzzo GJ, Suever JD, Geise BD, Patel AA, et al. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration. npj Digital Med 2018;1(1).
• [47] Shahangian B, Pourghassem H. Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybern Biomed Eng 2016;36(1):217–32.
• [48] Gautam A, Raman B, Raghuvanshi S. A hybrid approach for the delineation of brain lesion from CT images. Biocybern Biomed Eng 2018;38(3):504–18.
• [49] Isa IS, Sulaiman SN, Mustapha M, Noor KA, Karim,. Automatic contrast enhancement of brain MR images using Average Intensity Replacement based on Adaptive Histogram Equalization (AIR-AHE). Biocybern Biomed Eng 2017;37(1):24–34.
• [50] Devi CN, Chandrasekharan A, Sundararaman VK, Alex ZC. Automatic segmentation of infant brain MR images: With special reference to myelinated white matter,. Biocybern Biomed Eng 2017;37(1):143–58.
• [51] Sumathi R, Venkatesulu M, Arjunan SP. Extracting tumor in MR brain and breast image with Kapur’s entropy based Cuckoo Search Optimization and morphological reconstruction filters. Biocybern Biomed Eng 2018;38(4):918–30.
• [52] Raju AR, Suresh P, Rao RR. Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybern Biomed Eng 2018;38(3):646–60.
• [53] Alagarsamy S, Kamatchi K, Govindaraj V, Zhang Y-D, Thiyagarajan A. Multi-channeled MR brain image segmentation: A new automated approach combining BAT and clustering technique for better identification of heterogeneous tumors. Biocybern Biomed Eng 2019;39(4):1005–35.
• [54] Narayanan A, Rajasekaran MP, Zhang Y, Govindaraj V, Thiyagarajan A. Multi-channeled MR brain image segmentation: A novel double optimization approach combined with clustering technique for tumor identification and tissue segmentation. Biocybern Biomed Eng 2019;39(2):350–81.
• [55] Nayak DR, Dash R, Majhi B, Zhang Y. A hybrid regularized extreme learning machine for automated detection of pathological brain. Biocybern Biomed Eng 2019;39(3):880–92.
• [56] Devi S, Sahoo MN, Bakshi S. Manmath Narayan Sahoo, Sambit Bakshi, A novel privacy-supporting 2-class classification technique for brain MRI images. Biocybern Biomed Eng 2020;40(3):1022–35.
• [57] Subudhi A, Dash M, Sabut S. Automated segmentation and classification of brain stroke using expectation-maximization and random forest classifier. Biocybern Biomed Eng 2020;40(1):277–89.
• [58] Mishro PK, Agrawal S, Panda R, Abraham A. Ajith Abraham, A novel brightness preserving joint histogram equalization technique for contrast enhancement of brain MR images. Biocybern Biomed Eng 2021;41(2):540–53.
• [59] Lang EW, Ren Ya Z, Preul C, Hugo HH, Hempelmann RG, Buhl R, et al. Stroke pattern interpretation: the variability of hypertensive versus amyloid angiopathy hemorrhage. Cerebrovasc Dis 2001;12(2):121–30.
• [60] Aguilar MI, Brott TG. Update in intracerebral hemorrhage. Neurohospitalist 2011;1(3):148–59.
• [61] Kosior JC, Idris S, Dowlatshahi D, Alzawahmah M, Eesa M, Sharma P, et al. PREDICT/Sunnybrook CTA ICH Study Investigators. Quantomo: validation of a computer-assisted methodology for the volumetric analysis of intracerebral haemorrhage. Int J Stroke 2011;6(4):302–5.
• [62] Maeda AK et al. Hematoma volumes of spontaneous intracerebral hemorrhage: the ellipse (ABC/2) method yielded volumes smaller than those measured using the planimetric method. Arquivos de Neuro-Psiquiatria [online] 2013;71(8):540–4.
• [63] Zimmerman RD, Maldjian JA, Brun NC, Horvath B, Skolnick BE. Radiologic estimation of hematoma volume in intracerebral hemorrhage trial by CT scan. AJNR Am J Neuroradiol 2006;27(3):666–70.
• [64] Kidewell CS, Chalela JA, Saver JL, Starkman S, et al. Comparison of MRI and CT for detection of acute intracerebral hemorrhage. JAMA 2004;292:1823–30.
• [65] van Rooden S, van der Grond J, van den BoomR, Haan J, Linn J, Greenberg SM, et al. Descriptive analysis of the boston criteria applied to a dutch-type cerebral amyloid angiopathy population. Stroke 2009;40(9):3022–7.
• [66] Azmin S, Osman SS, Mukari S, Sahathevan R. Cerebral amyloid angiopathy: an important differential diagnosis of stroke in the elderly. Malays J Med Sci 2015;22(1):74–8.
• [67] Samarasekera N, Rodrigues MA, Toh PS, Salman R-S, Ai J. Imaging features of intracerebral hemorrhage with cerebral amyloid angiopathy: Systematic review and meta-analysis. PLoS ONE 2017;12(7):e0180923.
• [68] Li L, Poon MTC, Samarasekera NE, Perry LA, Moullaali TJ, Rodrigues MA, et al. Risks of recurrent stroke and all serious vascular events after spontaneous intracerebral haemorrhage: pooled analyses of two population-based studies. Lancet Neurol 2021;20(6):437–47.
• [69] Chinda B, Medvedev G, Siu W, Ester M, Arab A, Gu T, et al. Automation of CT-based haemorrhagic stroke assessment for improved clinical outcomes: study protocol and design. BMJ Open 2018;8(4):e020260.
• [70] Chawla M, Sharma S, Sivaswamy J, Kishore L. A method for automatic detection and classification of stroke from brain CT images. Annu Int Conf IEEE Eng Med Biol Soc 2009;2009:3581–4.
• [71] Chang PD, Kuoy E, Grinband J, Weinberg BD, Thompson M, Homo R, et al. Hybrid 3D/2D convolutional neural network for hemorrhage evaluation on head CT. AJNR Am J Neuroradiol 2018;39(9):1609–16.
• [72] Arab A, Chinda B, Medvedev G, Siu W, Guo H, Gu T, et al. A fast and fully automated deep learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole head CT. Sci Rep 2020;10:19389.
• [73] Raghavendra U, Pham TH, Gudigar A, Vidhya V, Rao BN, Sabut S, et al. Novel and accurate non-linear index for the automated detection of haemorrhagic brain stoke using CT images. Complex Intell Syst 2021.
• [74] Beaudin AE, McCreary CR, Mazerolle EL, Gee M, Sharma B, Subotic A, et al. Cerebrovascular reactivity across the entire brain in cerebral amyloid angiopathy. Neurology 2022;98(17): e1716–28.
• [75] Maramattom BV. Cerebral amyloid angiopathy with lobar haemorrhages and CAA-related inflammation in an Indian family. Cerebrovasc Dis Extra 2022;12(1):23–7.
• [76] Alan Z. Segal, Cerebral amyloid angiopathy and transient neurological events: can intracerebral hemorrhage risk be predicted?, Neurology Alert, Vol. 41, No. 8, 2022.
• [77] Schwarz G, Banerjee G, Hostettler IC, Ambler G, Seiffge DJ, Ozkan H, Browning S, Simister R,Wilson D, Cohen H, Yousry T, Salman RA, Lip GYH, Brown MM, Muir KW, Houlden H, Jäger R, Werring DJ. MRI and CT imaging biomarkers of cerebral amyloid angiopathy in lobar intracerebral hemorrhage. Int J Stroke 2022:17474930211062478.
• [78] Jessica Nye, Risk Factors for Lobar Intracerebral Hemorrhage and Death in Cerebral Amyloid Angiopathy, Neurology Advisor, January 2022.
• [79] Garg A, Ortega-Gutierrez S, Farooqui M, Nagaraja N. Recurrent intracerebral hemorrhage in patients with cerebral amyloid angiopathy: a propensity-matched case-control study. J Neurol 2022;269(4):2200–5.
• [80] Saito S, Tanaka M, Satoh-Asahara N, Carare RO, Ihara M. Taxifolin: A potential therapeutic agent for cerebral amyloid angiopathy. Front Pharmacol 2021;12 643357.
• [81] Inoue Y, Ando Y, Misumi Y, Ueda M. Current management and therapeutic strategies for cerebral amyloid angiopathy. Int J Mol Sci 2021;22(8):3869.
• [82] Wu JJ, Yao M, Ni J. Cerebral amyloid angiopathy-related inflammation: current status and future implications. Chin Med J (Engl) 2021;134(6):646–54.
• [83] Voigt S, Amlal S, Koemans EA, Rasing I, van Etten ES, van Zwet EW, van Buchem MA, Terwindt GM, van Walderveen MA, Wermer MJ. Spatial and temporal intracerebral hemorrhage patterns in Dutch-type hereditary cerebral amyloid angiopathy. Int J Stroke. 2021 Nov 18:17474930211057022.
• [84] Sanchez-Caro JM, de Lorenzo Martínez de Ubago I, de Celis Ruiz E, Arribas AB, Calviere L, Raposo N, Blancart RG, Fuentes B, Diez-Tejedor E, Rodriguez-Pardo J. Transient Focal Neurological Events in Cerebral Amyloid Angiopathy and the Long-term Risk of Intracerebral Hemorrhage and Death: A Systematic Review and Meta-analysis. JAMA Neurol 2022;79(1):38-47.
• [85] Renard D, Parvu T, Thouvenot E. Finger-like projections in lobar haemorrhage on early magnetic resonance imaging is associated with probable cerebral amyloid angiopathy. Cerebrovasc Dis 2019;47(3–4):121–6.
• [86] Rodrigues MA, Samarasekera N, Lerpiniere C, Humphreys C, McCarron MO, White PM, et al. The Edinburgh CT and genetic diagnostic criteria for lobar intracerebral haemorrhage associated with cerebral amyloid angiopathy: model development and diagnostic test accuracy study. Lancet Neurol 2018;17(3):232–40.
• [87] Wilson D, Hostettler IC, Ambler G, Banerjee G, Jäger HR, Werring DJ. Convexity subarachnoid haemorrhage has a high risk of intracerebral haemorrhage in suspected cerebral amyloid angiopathy. J Neurol 2017;264(4):664–73.
• [88] Barber PA, Hill MD, Eliasziw M, Demchuk AM, Pexman JH, Hudon ME, et al. ASPECTS Study Group. Imaging of the brain in acute ischaemic stroke: comparison of computed tomography and magnetic resonance diffusion-weighted imaging. J Neurol Neurosurg Psychiatry 2005;76(11):1528–33.
• [89] Vamsi B, Bhattacharyya D, Midhunchakkravarthy D, Kim J-Y. Early detection of hemorrhagic stroke using a lightweight deep learning neural network model. Traitement du Signal 2021;38(6).
• [90] Bandi Vamsi, Debnath Bhattacharyya, Divya Midhunchakkaravarthy, Detection of Brain Stroke Based on the Family History Using Machine Learning Techniques, Smart Technologies in Data Science and Communication, 17-31, 2021.
• [91] Bandi V, Bhattacharyya D, Midhunchakkravarthy D. Prediction of brain stroke severity using machine learning. Rev d’Intelligence Artif 2020;34(6):753–61.
• [92] Scherer M, Cordes J, Younsi A, Sahin YA, Gotz M, et al. Development and validation of an automatic segmentation algorithm for quantification of intracerebral hemorrhage. Stroke 2016;47:2776–82.
• [93] Baik M, Cha J, Ahn SS, Lee SK, Kim YD, Nam HS, et al. Dual-Energy Computed tomography quantification of extravasated iodine and hemorrhagic transformation after thrombectomy. J Stroke 2022;24(1):152–5.
• [94] Chen X, Lin S, Zhang X, Hu S, Wang X. Prognosis with non-contrast CT and CT Perfusion imaging in thrombolysis-treated acute ischemic stroke. Eur J Radiol 2022;149 110217.
• [95] Vajpeyee A, Tiwari S, Yadav LB, et al. Comparative analysis of functional outcome for CT-based versus MRI-based evaluation in acute ischemic stroke prior to mechanical thrombectomy. Egypt J Neurol Psychiatry Neurosurg 2022;58:28.
• [96] Moser FG, Todoran TM, Ryan M, Baker E, Gunnarsson C, Kellum JA. Hemorrhagic transformation rates following contrast media administration in patients hospitalized with ischemic stroke. AJNR Am J Neuroradiol 2022;43(3):381–7.
• [97] Zhang S, Wang J, Pei L, Liu K, Gao Y, Fang H, et al. Interpretable CNN for ischemic stroke subtype classification with active model adaptation. BMC Med Inform Decis Mak 2022;22(1):3.
• [98] Fang G, Huang Z, Wang Z. Predicting ischemic stroke outcome using deep learning approaches. Front Genet 2022;24(12) 827522.
• [99] Bridge CP, Bizzo BC, Hillis JM, Chin JK, Comeau DS, Gauriau R, et al. Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging. Sci Rep 2022;12(1):2154.
• [100] Daadi MM. Isolation and purification of self-renewable human neural stem cells from iPSCs for cell therapy in experimental model of ischemic stroke. Methods Mol Biol 2022;2389:165–75.
• [101] Zhang A, Ren M, DengW, Xi M, Tian L, Han Z, et al. Ischemia in intracerebral hemorrhage: A comparative study of small-vessel and large-vessel diseases. Ann Clin Transl Neurol2022;9(1):79–90.
• [102] Torrente D, Su EJ, Fredriksson L, Warnock M, Bushart D, Mann KM, et al. compartmentalized actions of the plasminogen activator inhibitors, PAI-1 and Nsp, in ischemic stroke. Transl Stroke Res 2022.
• [103] Li G, Ye C, Zhu Y, Zhang T, Gu J, Pan J, et al. Oxidative injury in ischemic stroke: A focus on NADPH oxidase 4. Oxid Med Cell Longev 2022;3(2022):1148874.
• [104] Yang YC, Liu SH, Hsu YH, Wu YL, Chu PT, Lin PC. Cerebrospinal fluid predictors of shunt-dependent hydrocephalus after hemorrhagic stroke: a systematic review and meta-analysis. Neurosurg Rev 2022.
• [105] Choi JM, Seo SY, Kim PJ, Kim YS, Lee SH, Sohn JH, et al. Prediction of hemorrhagic transformation after ischemic stroke using machine learning. J Pers Med 2021 Aug 30;11(9):863.
• [106] Patel A, Schreuder FBM, Klijn CJM, Prokop M, Ginneken BV, et al. Intracerebral haemorrage segmentation in non-contrast CT. Sci Rep 2019;9:17858.
• [107] Mrabet H, Belguith S, Alhomoud A, Jemai A. A survey of IoT security based on a layered architecture of sensing and data analysis. Sensors 2020;20(13):3625.
• [108] Belguith S, Kaaniche N, Laurent M, Jemai A, Attia R. Phoabe: Securely outsourcing multi-authority attribute based encryption with policy hidden for cloud assisted iot. Comput Netw 2018;133:141–56.
• [109] Belguith S, Kaaniche N, Hammoudeh M. Analysis of attribute-based cryptographic techniques and their application to protect cloud services. Trans Emerg Telecommun Technol 2019.
• [110] Liu X, Zhao M, Li S, Zhang F, TrappeW. A security framework for the internet of things in the future internet architecture. Future Internet 2017;9(3):27.
• [111] Demchuk AM, Dowlatshahi D, Rodriguez-Luna D, Molina CA, Blas YS, Dzialowski I, et al. Aviv RI; PREDICT/Sunnybrook ICH CTA study group. Prediction of haematoma growth and outcome in patients with intracerebral haemorrhage using the CT-angiography spot sign (PREDICT): a prospective observational study. Lancet Neurol 2012 Apr;11(4):307–14.
• [112] Wilson D, Ogungbemi A, Ambler G, Jones I, Werring DJ, Jager HR. Developing an algorithm to identify patients with intracerebral haemorrhage secondary to a macrovascular cause. Eur Stroke J 2017;2:369–76.
• [113] Gillebert CR, Humphreys GW, Mantini D. Automated delineation of stroke lesions using brain CT images. NeuroImage: Clinical 2014;4:540.