Boehme AK, Esenwa C, Elkind MS. Stroke risk factors, genetics, and prevention. Circ Res. 2017;120(3):472–95.
Zhao H-L, Huang Y. Lifetime risk of stroke in the global burden of disease study. N Engl J Med. 2019;380(14):1377–8.
World Health Organization: The top 10 causes of death. Accessed on 21.05.2023 (2020). https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death
Saver JL. Time is brain-quantified. Stroke. 2006;37(1):263–6.
Darehed D, Blom M, Glader E-L, Niklasson J, Norrving B, Eriksson M. In-hospital delays in stroke thrombolysis: every minute counts. Stroke. 2020;51(8):2536–9.
Sacco RL, Kasner SE, Broderick JP, Caplan LR, Connors J, Culebras A, Elkind MS, George MG, Hamdan AD, Higashida RT, et al. An updated definition of stroke for the 21st century: a statement for healthcare professionals from the American heart association/American stroke association. Stroke. 2013;44(7):2064–89.
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(5):1263–5.
Karthik R, Menaka R, Johnson A, Anand S. Neuroimaging and deep learning for brain stroke detection - a review of recent advancements and future prospects. Comput Methods Programs Biomed. 2020;197:105728.
Inamdar MA, Raghavendra U, Gudigar A, Chakole Y, Hegde A, Menon GR, Barua P, Palmer EE, Cheong KH, Chan WY, Ciaccio EJ, Acharya UR. A review on computer aided diagnosis of acute brain stroke. Sensors. 2021;21(24):8507. https://doi.org/10.3390/s21248507.
Zeng M, Oakden-Rayner L, Bird A, Smith L, Wu Z, Scroop R, Kleinig T, Jannes J, Jenkinson M, Palmer LJ. Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: a systematic review and meta-analysis. Front Neurol. 2022. https://doi.org/10.3389/fneur.2022.945813.
Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, et al. Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. PMLR, 2021:8748–8763
Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M-A, Lacroix T, Rozière B, Goyal N, Hambro E, Azhar F, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971 2023
Chawla M, Sharma S, Sivaswamy J, Kishore L. A method for automatic detection and classification of stroke from brain ct images. In: 2009 annual international conference of the IEEE engineering in medicine and biology society, pp. 3581–3584. IEEE (2009)
Rekik I, Allassonnière 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: Clinical. 2012;1(1):164–78.
Gupta N, Mittal A. Brain ischemic stroke segmentation: a survey. J Multi Discipl Eng Technol. 2014;8(1):1.
Mckinley R, Häni L, Gralla J, El-Koussy M, Bauer S, Arnold M, Fischer U, Jung S, Mattmann K, Reyes M, Wiest R. Fully automated stroke tissue estimation using random forest classifiers (FASTER). J Cerebr Blood Flow Metabol. 2017;37(8):2728–41.
Böhme L, Madesta F, Sentker T, Werner R. Combining good old random forest and deeplabv3+ for isles 2018 ct-based stroke segmentation. In: International MICCAI Brainlesion Workshop. Springer, 2018:335–342
Maier O, Wilms M, Gablentz J, Krämer U, Handels H. Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers. In: Medical Imaging 2014: Computer-Aided Diagnosis, 2014;9035:903504. ISOP
Lin T-Y, Goyal P, Girshick R, He K, Dollár P. Focal loss for dense object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017;2980–2988
Cai Z, Vasconcelos N. Cascade r-cnn: Delving into high quality object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018:6154–6162
Isensee F, Kickingereder P, Wick W, Bendszus M, Maier-Hein KH. Brain tumor segmentation and radiomics survival prediction: Contribution to the brats 2017 challenge. In: International MICCAI Brainlesion Workshop, 2017:287–297. Springer
Li X, Liu Z, Luo P, Change Loy C, Tang X. Not all pixels are equal: Difficulty-aware semantic segmentation via deep layer cascade. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2017:3193–3202
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016:770–778
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2018:7132–7141
Kamnitsas K, Bai W, Ferrante E, McDonagh S, Sinclair M, Pawlowski N, Rajchl M, Lee M, Kainz B, Rueckert D, et al. Ensembles of multiple models and architectures for robust brain tumour segmentation. In: International MICCAI Brainlesion Workshop, 2017:450–462. Springer
Hilbert A, Ramos L, Os H, Olabarriaga S, Tolhuisen M, Wermer M, Barros R, Schaaf I, Dippel D, Roos Y, et al. Data-efficient deep learning of radiological image data for outcome prediction after endovascular treatment of patients with acute ischemic stroke. Computers in Biology and Medicine, 2019:103516
Pinto A, Pereira S, Meier R, Wiest R, Alves V, Reyes M, Silva CA. Combining unsupervised and supervised learning for predicting the final stroke lesion. Med Image Anal. 2021;69:101888.
Hatamizadeh A, Nath V, Tang Y, Yang D, Roth HR, Xu D. Swin unetr: Swin transformers for semantic segmentation of brain tumors in mri images. In: International MICCAI Brainlesion Workshop, 2022:272–284. Springer
Amador K, Wilms M, Winder A, Fiehler J, Forkert ND. Predicting treatment-specific lesion outcomes in acute ischemic stroke from 4d CT perfusion imaging using spatio-temporal convolutional neural networks. Medical Image Analysis, 2022:102610 https://doi.org/10.1016/j.media.2022.102610
Samak ZA, Clatworthy P, Mirmehdi M. TranSOP: Transformer-based multimodal classification for stroke treatment outcome prediction. In: 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI). IEEE, 2023. https://doi.org/10.1109/isbi53787.2023.10230576
Gautam A, Raman B. Towards effective classification of brain hemorrhagic and ischemic stroke using cnn. Biomed Signal Process Control. 2021;63:102178.
Neethi A, Niyas S, Kannath SK, Mathew J, Anzar AM, Rajan J. Stroke classification from computed tomography scans using 3d convolutional neural network. Biomed Signal Process Control. 2022;76:103720.
Nishi H, Oishi N, Ishii A, Ono I, Ogura T, Sunohara T, Chihara H, Fukumitsu R, Okawa M, Yamana N, et al. Deep learning-derived high-level neuroimaging features predict clinical outcomes for large vessel occlusion. Stroke. 2020;51(5):1484–92.
Bacchi S, Zerner T, Oakden-Rayner L, Kleinig T, Patel S, Jannes J. Deep learning in the prediction of ischaemic stroke thrombolysis functional outcomes: A pilot study. Academic Radiology 2019
Samak ZA, Clatworthy P, Mirmehdi M. FeMA: feature matching auto-encoder for predicting ischaemic stroke evolution and treatment outcome. Comput Med Imag Graph. 2022;99:102089. https://doi.org/10.1016/j.compmedimag.2022.102089.
Ernst M, Boers AM, Aigner A, Berkhemer OA, Yoo AJ, Roos YB, Dippel DW, Lugt A, Oostenbrugge RJ, Zwam WH, et al. Association of computed tomography ischemic lesion location with functional outcome in acute large vessel occlusion ischemic stroke. Stroke. 2017;48(9):2426–33.
Nishi H, Oishi N, Ishii A, Ono I, Ogura T, Sunohara T, Chihara H, Fukumitsu R, Okawa M, Yamana N, et al. Predicting clinical outcomes of large vessel occlusion before mechanical thrombectomy using machine learning. Stroke. 2019;50(9):2379–88.
Sirsat MS, Fermé E, Câmara J. Machine learning for brain stroke: A review. J Stroke Cerebrovasc Dis. 2020;29(10):105162. https://doi.org/10.1016/j.jstrokecerebrovasdis.2020.105162.
Mainali S, Darsie ME, Smetana KS. Machine learning in action: stroke diagnosis and outcome prediction. Front Neurol. 2021;12:734345.
Kremers F, et al. Outcome prediction models for endovascular treatment of ischemic stroke: systematic review and external validation. Stroke. 2022;53(3):825–36. https://doi.org/10.1161/strokeaha.120.033445.
Wang X, Fan Y, Zhang N, Li J, Duan Y, Yang B. Performance of machine learning for tissue outcome prediction in acute ischemic stroke: a systematic review and meta-analysis. Front Neurol. 2022. https://doi.org/10.3389/fneur.2022.910259.
Hilbert A, Akay EM, Carlisle BG, Madai VI, Mutke MA, Frey D. Artificial intelligence for clinical decision support in acute ischemic stroke care: a systematic review 2022 https://doi.org/10.21203/rs.3.rs-1706474/v3
Winzeck S, Hakim A, McKinley R, Pinto JAADSR, Alves V, Silva C, Pisov M, Krivov E, Belyaev M, Monteiro M, Oliveira A, Choi Y, Paik MC, Kwon Y, Lee H, Kim BJ, Won J-H, Islam M, Ren H, Robben D, Suetens P, Gong E, Niu Y, Xu J, Pauly JM, Lucas C, Heinrich MP, Rivera LC, Castillo LS, Daza LA, Beers AL, Arbelaezs P, Maier O, Chang K, Brown JM, Kalpathy-Cramer J, Zaharchuk G, Wiest R, Reyes M. ISLES 2016 and 2017-benchmarking ischemic stroke lesion outcome prediction based on multispectral MRI. Front Neurol 2018;9
Alawneh JA, Jones PS, Mikkelsen IK, Cho T-H, Siemonsen S, Mouridsen K, Ribe L, Morris RS, Hjort N, Antoun N, et al. Infarction of ‘non-core-non-penumbral’tissue after stroke: multivariate modelling of clinical impact. Brain. 2011;134(6):1765–76.
Cheng B, Forkert ND, Zavaglia M, Hilgetag CC, Golsari A, Siemonsen S, Fiehler J, Pedraza S, Puig J, Cho T-H, et al. Influence of stroke infarct location on functional outcome measured by the modified rankin scale. Stroke. 2014;45(6):1695–702.
Lansberg MG, Straka M, Kemp S, Mlynash M, Wechsler LR, Jovin TG, Wilder MJ, Lutsep HL, Czartoski TJ, Bernstein RA, et al. MRI profile and response to endovascular reperfusion after stroke (defuse 2): a prospective cohort study. Lancet Neurol. 2012;11(10):860–7.
Goyal M, Menon BK, Van Zwam WH, Dippel DW, Mitchell PJ, Demchuk AM, Dávalos A, Majoie CB, Der Lugt A, De Miquel MA, et al. Endovascular thrombectomy after large-vessel ischaemic stroke: a meta-analysis of individual patient data from five randomised trials. The Lancet. 2016;387(10029):1723–31.
Lansberg MG, Christensen S, Kemp S, Mlynash M, Mishra N, Federau C, Tsai JP, Kim S, Nogueria RG, Jovin T, et al. Computed tomographic perfusion to predict response to recanalization in ischemic stroke. Ann Neurol. 2017;81(6):849–56.
Thamm T, Guo J, Rosenberg J, Liang T, Marks MP, Christensen S, Do HM, Kemp SM, Adair E, Eyngorn I, et al. Contralateral hemispheric cerebral blood flow measured with arterial spin labeling can predict outcome in acute stroke. Stroke. 2019;50(12):3408–15.
Fiehler J, Thomalla G, Bernhardt M, Kniep H, Berlis A, Dorn F, Eckert B, Kemmling A, Langner S, Remonda L, et al. Eraser: a thrombectomy study with predictive analytics end point. Stroke. 2019;50(5):1275–8.
Berkhemer OA, Fransen PSS, Beumer D, Berg LA, Lingsma HF, Yoo AJ, Schonewille WJ, Vos JA, Nederkoorn PJ, Wermer MJH, Walderveen MAA, Staals J, Hofmeijer J, Oostayen JA, Nijeholt GJ, Boiten J, Brouwer PA, Emmer BJ, Bruijn SF, Dijk LC, Kappelle LJ, Lo RH, Dijk EJ, Vries J, Kort PLM, Rooij WJJ, Berg JSP, Hasselt BAAM, Aerden LAM, Dallinga RJ, Visser MC, Bot JCJ, Vroomen PC, Eshghi O, Schreuder THCML, Heijboer RJJ, Keizer K, Tielbeek AV, Hertog HM, Gerrits DG, Berg-Vos RM, Karas GB, Steyerberg EW, Flach HZ, Marquering HA, Sprengers MES, Jenniskens SFM, Beenen LFM, Berg R, Koudstaal PJ, Zwam WH, Roos YBWEM, der A, Oostenbrugge RJ, Majoie CBLM, Dippel DWJ. A randomized trial of intraarterial treatment for acute ischemic stroke. New England J Med 2015;372(1):11–20
Compagne KC, Kappelhof M, Hinsenveld WH, Brouwer J, Goldhoorn R-JB, Uyttenboogaart M, Bokkers RP, Schonewille WJ, Martens JM, Hofmeijer J, et al. Improvements in endovascular treatment for acute ischemic stroke: a longitudinal study in the MR clean registry. Stroke. 2022;53(6):1863–72.
LeCouffe NE, Kappelhof M, Treurniet KM, Rinkel LA, Bruggeman AE, Berkhemer OA, Wolff L, Voorst H, Tolhuisen ML, Dippel DW, et al. A randomized trial of intravenous alteplase before endovascular treatment for stroke. N Engl J Med. 2021;385(20):1833–44.
Debs N, Cho T-H, Rousseau D, Berthezène Y, Buisson M, Eker O, Mechtouff L, Nighoghossian N, Ovize M, Frindel C. Impact of the reperfusion status for predicting the final stroke infarct using deep learning. NeuroImage: Clinical. 2021;29:102548.
Comments (0)