{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,22]],"date-time":"2026-03-22T07:28:19Z","timestamp":1774164499553,"version":"3.50.1"},"reference-count":129,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s11517-022-02758-y","type":"journal-article","created":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T05:17:39Z","timestamp":1674883059000},"page":"1257-1297","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["A survey of machine learning-based methods for COVID-19 medical image analysis"],"prefix":"10.1007","volume":"61","author":[{"given":"Kashfia","family":"Sailunaz","sequence":"first","affiliation":[]},{"given":"Tansel","family":"\u00d6zyer","sequence":"additional","affiliation":[]},{"given":"Jon","family":"Rokne","sequence":"additional","affiliation":[]},{"given":"Reda","family":"Alhajj","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"2758_CR1","unstructured":"World Health Organization (2022) Origins of the SARS-CoV-2 virus, Available at https:\/\/www.who.int\/health-topics\/coronavirus\/origins-of-the-virus (2022\/09\/09)"},{"key":"2758_CR2","unstructured":"World Health Organization (2021) Who-convened global study of origins of SARS-CoV-2: China part"},{"key":"2758_CR3","unstructured":"World Health Organization (2022) Who director-general\u2019s opening remarks at the media briefing on COVID-19 - 11 March 2020, Available at https:\/\/www.who.int\/director-general\/speeches\/detail\/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 . Accessed 09 Sep 2022"},{"key":"2758_CR4","unstructured":"Worldometer (2022) COVID-19 coronavirus pandemic, Available at https:\/\/www.worldometers.info\/coronavirus\/ (2022\/11\/06)"},{"issue":"4","key":"2758_CR5","doi-asserted-by":"publisher","first-page":"372","DOI":"10.3390\/v12040372","volume":"12","author":"Y Jin","year":"2020","unstructured":"Jin Y, Yang H, Ji W, Wu W, Chen S, Zhang W, Duan G (2020) Virology, epidemiology, pathogenesis, and control of COVID-19. Viruses 12(4):372","journal-title":"Viruses"},{"issue":"6","key":"2758_CR6","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1080\/10408363.2020.1783198","volume":"57","author":"M Ciotti","year":"2020","unstructured":"Ciotti M, Ciccozzi M, Terrinoni A, Jiang WC, Wang CB, Bernardini S (2020) The COVID-19 pandemic. Crit Rev Clin Lab Sci 57(6):365\u2013388","journal-title":"Crit Rev Clin Lab Sci"},{"key":"2758_CR7","unstructured":"World Health Organization (2022) Tracking sars-cov-2 variants, Available at https:\/\/www.who.int\/en\/activities\/tracking-SARS-CoV-2-variants\/. Accessed 09 Sep 2022"},{"key":"2758_CR8","doi-asserted-by":"publisher","first-page":"149 808","DOI":"10.1109\/ACCESS.2020.3016780","volume":"8","author":"MJ Horry","year":"2020","unstructured":"Horry MJ, Chakraborty S, Paul M, Ulhaq A, Pradhan B, Saha M, Shukla N (2020) COVID-19 detection through transfer learning using multimodal imaging data. IEEE Access 8:149 808\u2013 149 824","journal-title":"IEEE Access"},{"issue":"5","key":"2758_CR9","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1080\/14737159.2020.1757437","volume":"20","author":"A Tahamtan","year":"2020","unstructured":"Tahamtan A, Ardebili A (2020) Real-time RT-PCR in COVID-19 detection: issues affecting the results. Expert Rev Mol Diagn 20(5):453\u2013454","journal-title":"Expert Rev Mol Diagn"},{"issue":"2","key":"2758_CR10","doi-asserted-by":"publisher","first-page":"E63","DOI":"10.1148\/radiol.2020203173","volume":"298","author":"EA Akl","year":"2021","unstructured":"Akl EA, Bla\u017ei\u0107 I, Yaacoub S, Frija G, Chou R, Appiah JA, Fatehi M, Flor N, Hitti E, Jafri H, Jin ZY (2021) Use of chest imaging in the diagnosis and management of COVID-19: a who rapid advice guide. Radiology 298(2):E63\u2013E69","journal-title":"Radiology"},{"key":"2758_CR11","unstructured":"News-Medical.Net (2022) Transfer learning exploits chest-xray to diagnose COVID-19 pneumonia, Available at https:\/\/www.news-medical.net\/news\/20201218\/Transfer-learning-exploits-chest-Xray-to-diagnose-COVID-19-pneumonia.aspx. Accessed 09 Sep 2022"},{"key":"2758_CR12","doi-asserted-by":"publisher","first-page":"102365","DOI":"10.1016\/j.bspc.2020.102365","volume":"64","author":"SR Nayak","year":"2021","unstructured":"Nayak SR, Nayak DR, Sinha U, Arora V, Pachori RB (2021) Application of deep learning techniques for detection of COVID-19 cases using chest x-ray images: a comprehensive study. Biomed Signal Process Control 64:102365","journal-title":"Biomed Signal Process Control"},{"issue":"11","key":"2758_CR13","doi-asserted-by":"publisher","first-page":"e042946","DOI":"10.1136\/bmjopen-2020-042946","volume":"10","author":"A Borakati","year":"2020","unstructured":"Borakati A, Perera A, Johnson J, Sood T (2020) Diagnostic accuracy of x-ray versus CT in COVID-19: a propensity-matched database study. BMJ Open 10(11):e042946","journal-title":"BMJ Open"},{"key":"2758_CR14","doi-asserted-by":"crossref","unstructured":"Sverzellati N, Ryerson CJ, Milanese G, Renzoni EA, Volpi A, Spagnolo P, Bonella F, Comelli I, Affanni P, Veronesi L, Manna C (2021) Chest x-ray or CT for COVID-19 pneumonia? Comparative study in a simulated triage setting. European Respiratory Journal","DOI":"10.1183\/13993003.04188-2020"},{"issue":"1","key":"2758_CR15","doi-asserted-by":"publisher","first-page":"80","DOI":"10.2214\/AJR.20.23513","volume":"216","author":"Y Zhang","year":"2021","unstructured":"Zhang Y, Xue H, Wang M, He N, Lv Z, Cui L (2021) Lung ultrasound findings in patients with coronavirus disease (COVID-19). Am J Roentgenol 216(1):80\u201384","journal-title":"Am J Roentgenol"},{"issue":"7","key":"2758_CR16","doi-asserted-by":"publisher","first-page":"1848","DOI":"10.1148\/rg.2020200159","volume":"40","author":"TC Kwee","year":"2020","unstructured":"Kwee TC, Kwee RM (2020) Chest CT in COVID-19: what the radiologist needs to know. RadioGraphics 40(7):1848\u2013 1865","journal-title":"RadioGraphics"},{"issue":"2","key":"2758_CR17","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1007\/s10489-020-01867-1","volume":"51","author":"T Zebin","year":"2021","unstructured":"Zebin T, Rezvy S (2021) COVID-19 detection and disease progression visualization: deep learning on chest x-rays for classification and coarse localization. Appl Intell 51(2):1010\u20131021","journal-title":"Appl Intell"},{"key":"2758_CR18","unstructured":"VIDA (2022) Lung intelligence for COVID-19, Available at https:\/\/hub.vidalung.ai\/covid-19. Accessed 09 Sep 2022"},{"key":"2758_CR19","unstructured":"InferVision (2022) A.I. Solutions, Available at https:\/\/global.infervision.com\/product\/5\/. Accessed 09 Sep 2022"},{"key":"2758_CR20","unstructured":"NVIDIA (2022) Clara COVID-19, Available at https:\/\/ngc.nvidia.com\/catalog\/collections\/nvidia:claracovid19. Accessed 09 Sep 2022"},{"key":"2758_CR21","unstructured":"Thirona (2022) Artificial intelligence to screen for COVID-19 on CT- and x-ray images, Available at https:\/\/thirona.eu\/cad4covid\/. Accessed 09 Sep 2022"},{"key":"2758_CR22","unstructured":"Ai4Networks (2022) An artificial intelligence powered app for detecting COVID-19 from cough sound, Available at https:\/\/www.ai4networks.com\/covid19.php. Accessed 09 Sep 2022"},{"key":"2758_CR23","unstructured":"Innobiochips (2022) Covidiag, Available at http:\/\/www.innobiochips.fr\/applications\/covidiag. Accessed 09 Sep 2022"},{"key":"2758_CR24","unstructured":"Li S (2022) COVID-19 assistant discrimination, Available at http:\/\/lishuyan.lzu.edu.cn\/COVID2019_2\/. Accessed 09 Sep 2022"},{"key":"2758_CR25","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp 618\u2013 626","DOI":"10.1109\/ICCV.2017.74"},{"key":"2758_CR26","unstructured":"NCBI (2022) Pubmed, Available at https:\/\/pubmed.ncbi.nlm.nih.gov\/. Accessed 09 Sep 2022"},{"key":"2758_CR27","unstructured":"Google (2022) Google Scholar, Available at https:\/\/scholar.google.com\/. Accessed 09 Sep 2022"},{"key":"2758_CR28","doi-asserted-by":"publisher","first-page":"S36","DOI":"10.1016\/j.metabol.2017.01.011","volume":"69","author":"P Hamet","year":"2017","unstructured":"Hamet P, Tremblay J (2017) Artificial intelligence in medicine. Metabolism 69:S36\u2013S40","journal-title":"Metabolism"},{"issue":"3","key":"2758_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00592-x","volume":"2","author":"IH Sarker","year":"2021","unstructured":"Sarker IH (2021) Machine learning: algorithms, real-world applications and research directions. SN Computer Science 2(3):1\u201321","journal-title":"SN Computer Science"},{"issue":"1","key":"2758_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s42979-021-00965-2","volume":"3","author":"RS Abirami","year":"2022","unstructured":"Abirami RS, Kumar GS (2022) Comparative study based on analysis of coronavirus disease (COVID-19) detection and prediction using machine learning models. SN Computer Science 3(1):1\u20138","journal-title":"SN Computer Science"},{"issue":"4","key":"2758_CR31","doi-asserted-by":"publisher","first-page":"570","DOI":"10.3348\/kjr.2017.18.4.570","volume":"18","author":"J Lee","year":"2017","unstructured":"Lee J, Jun S, Cho Y, Lee H, Kim GB, Seo JB, Kim N (2017) Deep learning in medical imaging: general overview. Korean J Radiol 18(4):570\u2013584","journal-title":"Korean J Radiol"},{"issue":"11","key":"2758_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-018-1088-1","volume":"42","author":"SM Anwar","year":"2018","unstructured":"Anwar SM, Majid M, Qayyum A, Awais M, Alnowami M, Khan MK (2018) Medical image analysis using convolutional neural networks: a review. J Med Syst 42(11):1\u201313","journal-title":"J Med Syst"},{"key":"2758_CR33","doi-asserted-by":"publisher","first-page":"44 111","DOI":"10.1109\/ACCESS.2020.2978090","volume":"8","author":"M Zhao","year":"2020","unstructured":"Zhao M, Chang CH, Xie W, Xie Z, Hu J (2020) Cloud shape classification system based on multi-channel CNN and improved FDM. IEEE Access 8:44 111\u201344 124","journal-title":"IEEE Access"},{"issue":"9","key":"2758_CR34","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/JBHI.2020.2986376","volume":"24","author":"Q Xu","year":"2020","unstructured":"Xu Q, Zeng Y, Tang W, Peng W, Xia T, Li Z, Teng F, Li W, Guo J (2020) Multi-task joint learning model for segmenting and classifying tongue images using a deep neural network. IEEE J Biomed Health Inform 24(9):2481\u20132489","journal-title":"IEEE J Biomed Health Inform"},{"key":"2758_CR35","doi-asserted-by":"publisher","first-page":"15 844","DOI":"10.1109\/ACCESS.2018.2810849","volume":"6","author":"Q Zheng","year":"2018","unstructured":"Zheng Q, Yang M, Yang J, Zhang Q, Zhang X (2018) Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process. IEEE Access 6:15 844\u201315 869","journal-title":"IEEE Access"},{"key":"2758_CR36","first-page":"4706576","volume":"2020","author":"Q Zheng","year":"2020","unstructured":"Zheng Q, Yang M, Tian X, Jiang N, Wang D (2020) A full stage data augmentation method in deep convolutional neural network for natural image classification. Discrete Dyn Nat So 2020:4706576","journal-title":"Discrete Dyn Nat So"},{"issue":"10","key":"2758_CR37","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2009","unstructured":"Pan SJ, Yang Q (2009) A survey on transfer learning. IEEE Trans Knowl Data Eng 22 (10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2758_CR38","doi-asserted-by":"publisher","first-page":"123 649","DOI":"10.1109\/ACCESS.2020.3005687","volume":"8","author":"B Jin","year":"2020","unstructured":"Jin B, Cruz L, Gon\u00e7alves N (2020) Deep facial diagnosis: deep transfer learning from face recognition to facial diagnosis. IEEE Access 8:123 649\u2013123 661","journal-title":"IEEE Access"},{"issue":"1","key":"2758_CR39","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","volume":"109","author":"F Zhuang","year":"2020","unstructured":"Zhuang F, Qi Z, Duan K, Xi D, Zhu Y, Zhu H, Xiong H, He Q (2020) A comprehensive survey on transfer learning. Proc IEEE 109(1):43\u201376","journal-title":"Proc IEEE"},{"key":"2758_CR40","doi-asserted-by":"crossref","unstructured":"Seliya N, Khoshgoftaar TM, Hulse JV (2009) A study on the relationships of classifier performance metrics. In: 2009 21st IEEE International Conference on Tools with Artificial Intelligence, IEEE, pp 59\u201366","DOI":"10.1109\/ICTAI.2009.25"},{"issue":"1","key":"2758_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12880-015-0068-x","volume":"15","author":"AA Taha","year":"2015","unstructured":"Taha AA, Hanbury A (2015) Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 15(1):1\u201328","journal-title":"BMC Med Imaging"},{"key":"2758_CR42","unstructured":"Narkhede S (2022) Understanding AUC - ROC curve, Available at https:\/\/towardsdatascience.com\/understanding-auc-roc-curve-68b2303cc9c5. Accessed 09 Sep 2022"},{"issue":"3","key":"2758_CR43","doi-asserted-by":"publisher","first-page":"1117","DOI":"10.3390\/ijerph18031117","volume":"18","author":"T Alafif","year":"2021","unstructured":"Alafif T, Tehame AM, Bajaba S, Barnawi A, Zia S (2021) Machine and deep learning towards COVID-19 diagnosis and treatment: survey, challenges, and future directions. Int J Environ Res Public Health 18(3):1117","journal-title":"Int J Environ Res Public Health"},{"key":"2758_CR44","unstructured":"Mangal A, Kalia S, Rajgopal H, Rangarajan K, Namboodiri V, Banerjee S, Arora C (2020) CovidAID: COVID-19 detection using chest x-ray, arXiv preprint arXiv:2004.09803"},{"key":"2758_CR45","unstructured":"NVIDIA (2022) Clara COVID-19, Available at https:\/\/ngc.nvidia.com\/catalog\/collections\/nvidia:claracovid19. Accessed 09 Sep 2022"},{"issue":"2","key":"2758_CR46","doi-asserted-by":"publisher","first-page":"E65","DOI":"10.1148\/radiol.2020200905","volume":"296","author":"L Li","year":"2020","unstructured":"Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, Bai J, Lu Y, Fang Z, Song Q, Cao K (2020) Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology 296(2):E65\u2013E71","journal-title":"Radiology"},{"issue":"1","key":"2758_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3381014","volume":"4","author":"FA Hossain","year":"2020","unstructured":"Hossain FA, Lover AA, Corey GA, Reich NG, Rahman T (2020) Flusense: a contactless syndromic surveillance platform for influenza-like illness in hospital waiting areas. Proc ACM Interact Mob Wearable Ubiquitous Technol 4(1):1\u201328","journal-title":"Proc ACM Interact Mob Wearable Ubiquitous Technol"},{"issue":"9","key":"2758_CR48","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.3390\/jcm10091961","volume":"10","author":"M Islam","year":"2021","unstructured":"Islam M, Poly TN, Alsinglawi B, Lin MC, Hsu MH, Li YCJ (2021) A state-of-the-art survey on artificial intelligence to fight COVID-19. J Clin Med 10(9):1961","journal-title":"J Clin Med"},{"issue":"5","key":"2758_CR49","doi-asserted-by":"publisher","first-page":"2908","DOI":"10.1007\/s10489-020-02102-7","volume":"51","author":"J Nayak","year":"2021","unstructured":"Nayak J, Naik B, Dinesh P, Vakula K, Rao BK, Ding W, Pelusi D (2021) Intelligent system for COVID-19 prognosis: a state-of-the-art survey. Appl Intell 51(5):2908\u2013 2938","journal-title":"Appl Intell"},{"key":"2758_CR50","doi-asserted-by":"crossref","unstructured":"Lalmuanawma S, Hussain J, Chhakchhuak L (2020) Applications of machine learning and artificial intelligence for COVID-19 (SARS-CoV-2) pandemic: a review, Chaos, Solitons & Fractals, p 110059","DOI":"10.1016\/j.chaos.2020.110059"},{"key":"2758_CR51","doi-asserted-by":"publisher","first-page":"185","DOI":"10.3389\/fcvm.2021.638011","volume":"8","author":"H Mohammad-Rahimi","year":"2021","unstructured":"Mohammad-Rahimi H, Nadimi M, Ghalyanchi-Langeroudi A, Taheri M, Ghafouri-Fard S (2021) Application of machine learning in diagnosis of COVID-19 through x-ray and CT images: a scoping review. Front Cardiovasc Med 8:185","journal-title":"Front Cardiovasc Med"},{"issue":"36","key":"2758_CR52","doi-asserted-by":"publisher","first-page":"e26855","DOI":"10.1097\/MD.0000000000026855","volume":"100","author":"F Zhang","year":"2021","unstructured":"Zhang F (2021) Application of machine learning in CT images and x-rays of COVID-19 pneumonia. Medicine 100(36):e26855","journal-title":"Medicine"},{"key":"2758_CR53","doi-asserted-by":"publisher","first-page":"105233","DOI":"10.1016\/j.compbiomed.2022.105233","volume":"143","author":"N Subramanian","year":"2022","unstructured":"Subramanian N, Elharrouss O, Al-Maadeed S, Chowdhury M (2022) A review of deep learning-based detection methods for COVID-19. Comput Biol Med 143:105233","journal-title":"Comput Biol Med"},{"key":"2758_CR54","doi-asserted-by":"crossref","unstructured":"Ozsahin I, Sekeroglu B, Musa MS, Mustapha MT, Ozsahin DU (2020) Review on diagnosis of COVID-19 from chest CT images using artificial intelligence. Comput Math Methods Med, 2020","DOI":"10.1155\/2020\/9756518"},{"key":"2758_CR55","doi-asserted-by":"crossref","unstructured":"Alghamdi H, Amoudi G, Elhag S, Saeedi K, Nasser J (2021) Deep learning approaches for detecting COVID-19 from chest x-ray images: a survey. IEEE Access","DOI":"10.2196\/preprints.26506"},{"issue":"6","key":"2758_CR56","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1109\/TLA.2021.9451237","volume":"19","author":"OL de Sousa","year":"2021","unstructured":"de Sousa OL, Magalh\u00e3es DM, Vieira PDA, Silva R (2021) Deep learning in image analysis for COVID-19 diagnosis: a survey. IEEE Lat Am Trans 19(6):925\u2013936","journal-title":"IEEE Lat Am Trans"},{"key":"2758_CR57","doi-asserted-by":"crossref","unstructured":"Bhattacharya S, Maddikunta PKR, Pham Q, Gadekallu TR, Chowdhary CL, Alazab M, Piran MJ (2021) Deep learning and medical image processing for coronavirus (COVID-19) pandemic: a survey, vol 65","DOI":"10.1016\/j.scs.2020.102589"},{"issue":"4","key":"2758_CR58","doi-asserted-by":"publisher","first-page":"569","DOI":"10.1016\/j.dsx.2020.05.008","volume":"14","author":"A Kumar","year":"2020","unstructured":"Kumar A, Gupta PK, Srivastava A (2020) A review of modern technologies for tackling COVID-19 pandemic. Diabetes Metab Syndr: Clinical Research & Reviews 14(4):569\u2013573","journal-title":"Diabetes Metab Syndr: Clinical Research & Reviews"},{"key":"2758_CR59","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"14","author":"F Shi","year":"2020","unstructured":"Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, He K, Shi Y, Shen D (2020) Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng 14:4\u201315","journal-title":"IEEE Rev Biomed Eng"},{"key":"2758_CR60","doi-asserted-by":"publisher","first-page":"109947","DOI":"10.1016\/j.chaos.2020.109947","volume":"138","author":"H Swapnarekha","year":"2020","unstructured":"Swapnarekha H, Behera HS, Nayak J, Naik B (2020) Role of intelligent computing in COVID-19 prognosis: a state-of-the-art review. Chaos, Solitons & Fractals 138:109947","journal-title":"Chaos, Solitons & Fractals"},{"key":"2758_CR61","doi-asserted-by":"crossref","unstructured":"Albahri OS, Zaidan AA, Albahri AS, Zaidan BB, Abdulkareem KH, Al-Qaysi ZT, Alamoodi AH, Aleesa AM, Chyad MA, Alesa RM, Kim LC (2020) Systematic review of artificial intelligence techniques in the detection and classification of COVID-19 medical images in terms of evaluation and benchmarking: taxonomy analysis, challenges, future solutions and methodological aspects, Journal of Infection and Public Health","DOI":"10.1016\/j.jiph.2020.06.028"},{"key":"2758_CR62","doi-asserted-by":"publisher","first-page":"101830","DOI":"10.1016\/j.sysarc.2020.101830","volume":"108","author":"A Sufian","year":"2020","unstructured":"Sufian A, Ghosh A, Sadiq AS, Smarandache F (2020) A survey on deep transfer learning to edge computing for mitigating the COVID-19 pandemic. J Syst Architect 108:101830","journal-title":"J Syst Architect"},{"issue":"11","key":"2758_CR63","doi-asserted-by":"publisher","first-page":"3913","DOI":"10.1007\/s10489-020-01770-9","volume":"50","author":"Y Mohamadou","year":"2020","unstructured":"Mohamadou Y, Halidou A, Kapen PT (2020) A review of mathematical modeling, artificial intelligence and datasets used in the study, prediction and management of COVID-19. Appl Intell 50(11):3913\u20133925","journal-title":"Appl Intell"},{"key":"2758_CR64","first-page":"12","volume":"11","author":"J Shuja","year":"2020","unstructured":"Shuja J, Alanazi E, Alasmary W, Alashaikh A (2020) COVID-19 datasets: a survey and future challenges. Development 11:12","journal-title":"Development"},{"key":"2758_CR65","doi-asserted-by":"crossref","unstructured":"Latif S, Usman M, Manzoor S, Iqbal W, Qadir J, Tyson G, Castro I, Razi A, Boulos MNK, Weller A, Crowcroft J (2020) Leveraging data science to combat COVID-19: a comprehensive review, IEEE Transactions on Artificial Intelligence","DOI":"10.36227\/techrxiv.12212516"},{"issue":"4","key":"2758_CR66","first-page":"2672","volume":"18","author":"HA Hussein","year":"2021","unstructured":"Hussein HA, Abdulazeez AM (2021) COVID-19 pandemic datasets based on machine learning clustering algorithms: a review. PalArch\u2019s J Archaeol Egypt\/ Egyptol 18(4):2672\u20132700","journal-title":"PalArch\u2019s J Archaeol Egypt\/ Egyptol"},{"key":"2758_CR67","doi-asserted-by":"publisher","first-page":"113909","DOI":"10.1016\/j.eswa.2020.113909","volume":"165","author":"TB Chandra","year":"2021","unstructured":"Chandra TB, Verma K, Singh BK, Jain D, Netam SS (2021) Coronavirus disease (COVID-19) detection in chest x-ray images using majority voting based classifier ensemble. Expert Syst Appl 165:113909","journal-title":"Expert Syst Appl"},{"key":"2758_CR68","doi-asserted-by":"crossref","unstructured":"Singh AK, Kumar A, Mahmud M, Kaiser MS, Kishore A (2021) COVID-19 infection detection from chest x-ray images using hybrid social group optimization and support vector classifier. Cognit Compu, 1\u201313","DOI":"10.1007\/s12559-021-09848-3"},{"key":"2758_CR69","doi-asserted-by":"crossref","unstructured":"Aradhya VNM, Mahmud M, Guru DS, Agarwal B, Kaiser MS (2021) One-shot cluster-based approach for the detection of COVID\u201319 from chest x\u2013ray images. Cognit Compu, 1\u20139","DOI":"10.20944\/preprints202007.0656.v1"},{"issue":"4","key":"2758_CR70","doi-asserted-by":"publisher","first-page":"5423","DOI":"10.1007\/s11042-020-09894-3","volume":"80","author":"H Yasar","year":"2021","unstructured":"Yasar H, Ceylan M (2021) A novel comparative study for detection of COVID-19 on ct lung images using texture analysis, machine learning, and deep learning methods. Multimed Tools Appl 80(4):5423\u20135447","journal-title":"Multimed Tools Appl"},{"key":"2758_CR71","doi-asserted-by":"publisher","first-page":"114054","DOI":"10.1016\/j.eswa.2020.114054","volume":"164","author":"AM Ismael","year":"2021","unstructured":"Ismael AM, \u015eeng\u00fcr A (2021) Deep learning approaches for COVID-19 detection based on chest x-ray images. Expert Syst Appl 164:114054","journal-title":"Expert Syst Appl"},{"key":"2758_CR72","doi-asserted-by":"publisher","first-page":"102622","DOI":"10.1016\/j.bspc.2021.102622","volume":"68","author":"D Sharifrazi","year":"2021","unstructured":"Sharifrazi D, Alizadehsani R, Roshanzamir M, Joloudari JH, Shoeibi A, Jafari M, Hussain S, Sani ZA, Hasanzadeh F, Khozeimeh F, Khosravi A (2021) Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using x-ray images. Biomed Signal Process Control 68:102622","journal-title":"Biomed Signal Process Control"},{"key":"2758_CR73","doi-asserted-by":"publisher","first-page":"103805","DOI":"10.1016\/j.compbiomed.2020.103805","volume":"121","author":"M To\u011fa\u00e7ar","year":"2020","unstructured":"To\u011fa\u00e7ar M, Ergen B, C\u00f6mert Z (2020) COVID-19 detection using deep learning models to exploit social mimic optimization and structured chest x-ray images using fuzzy color and stacking approaches. Comput Biol Med 121:103805","journal-title":"Comput Biol Med"},{"key":"2758_CR74","doi-asserted-by":"publisher","first-page":"166405","DOI":"10.1016\/j.ijleo.2021.166405","volume":"231","author":"AS Elkorany","year":"2021","unstructured":"Elkorany AS, Elsharkawy ZF (2021) Covidetection-net: a tailored COVID-19 detection from chest radiography images using deep learning. Optik 231:166405","journal-title":"Optik"},{"key":"2758_CR75","doi-asserted-by":"publisher","first-page":"105581","DOI":"10.1016\/j.cmpb.2020.105581","volume":"196","author":"AI Khan","year":"2020","unstructured":"Khan AI, Shah JL, Bhat MM (2020) Coronet: a deep neural network for detection and diagnosis of COVID-19 from chest x-ray images. Comput Methods Programs Biomed 196:105581","journal-title":"Comput Methods Programs Biomed"},{"key":"2758_CR76","doi-asserted-by":"publisher","first-page":"103869","DOI":"10.1016\/j.compbiomed.2020.103869","volume":"122","author":"T Mahmud","year":"2020","unstructured":"Mahmud T, Rahman MA, Fattah SA (2020) Covxnet: a multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest x-ray images with transferable multi-receptive feature optimization. Comput Biol Med 122:103869","journal-title":"Comput Biol Med"},{"key":"2758_CR77","doi-asserted-by":"publisher","first-page":"103182","DOI":"10.1016\/j.bspc.2021.103182","volume":"71","author":"A Bhattacharyya","year":"2022","unstructured":"Bhattacharyya A, Bhaik D, Kumar S, Thakur P, Sharma R, Pachori RB (2022) A deep learning based approach for automatic detection of COVID-19 cases using chest x-ray images. Biomed Signal Process Control 71:103182","journal-title":"Biomed Signal Process Control"},{"key":"2758_CR78","doi-asserted-by":"crossref","unstructured":"Loey M, El-Sappagh S, Mirjalili S (2022) Bayesian-based optimized deep learning model to detect COVID-19 patients using chest x-ray image data, vol 142","DOI":"10.1016\/j.compbiomed.2022.105213"},{"issue":"4","key":"2758_CR79","doi-asserted-by":"publisher","first-page":"1480","DOI":"10.3390\/s21041480","volume":"21","author":"M Ahsan","year":"2021","unstructured":"Ahsan M, Based M, Haider J, Kowalski M (2021) COVID-19 detection from chest x-ray images using feature fusion and deep learning. Sensors 21(4):1480","journal-title":"Sensors"},{"issue":"1","key":"2758_CR80","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/s10489-020-01831-z","volume":"51","author":"V Perumal","year":"2021","unstructured":"Perumal V, Narayanan V, Rajasekar SJS (2021) Detection of COVID-19 using CXR and CT images using transfer learning and Haralick features. Appl Intell 51(1):341\u2013358","journal-title":"Appl Intell"},{"issue":"1","key":"2758_CR81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-56847-4","volume":"10","author":"L Wang","year":"2020","unstructured":"Wang L, Linda ZQLIN, Wong A (2020) COVID-net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest x-ray images. Sci Rep 10(1):1\u201312","journal-title":"Sci Rep"},{"key":"2758_CR82","doi-asserted-by":"publisher","first-page":"110495","DOI":"10.1016\/j.chaos.2020.110495","volume":"142","author":"E Hussain","year":"2021","unstructured":"Hussain E (2021) Corodet: a deep learning based classification for COVID-19 detection using chest x-ray images. Chaos Solitons Fractals 142:110495","journal-title":"Chaos Solitons Fractals"},{"key":"2758_CR83","doi-asserted-by":"crossref","unstructured":"Narin A, Kaya C, Pamuk Z (2021) Automatic detection of coronavirus disease (COVID-19) using x-ray images and deep convolutional neural networks. Pattern Anal Applic, 1\u201314","DOI":"10.1007\/s10044-021-00984-y"},{"key":"2758_CR84","doi-asserted-by":"crossref","unstructured":"Luz E, Silva P, Silva R, Silva L, Guimar\u00e3es J, Miozzo G, Moreira G, Menotti D (2021) Towards an effective and efficient deep learning model for COVID-19 patterns detection in x-ray images. Research on Biomedical Engineering, 1\u201314","DOI":"10.1007\/s42600-021-00151-6"},{"key":"2758_CR85","doi-asserted-by":"crossref","unstructured":"Das AK, Ghosh S, Thunder S, Dutta R, Agarwal S, Chakrabarti A (2021) Automatic COVID-19 detection from x-ray images using ensemble learning with convolutional neural network. Pattern Anal Applic, 1\u201314","DOI":"10.21203\/rs.3.rs-51360\/v1"},{"key":"2758_CR86","doi-asserted-by":"crossref","unstructured":"Tang S, Wang C, Nie J, Kumar N, Zhang Y, Xiong Z, Barnawi A (2021) EDL-COVID: ensemble deep learning for COVID-19 cases detection from chest x-ray images. In: IEEE Transactions on Industrial Informatics","DOI":"10.1109\/TII.2021.3057683"},{"key":"2758_CR87","doi-asserted-by":"crossref","unstructured":"Zhou T, Lu H, Yang Z, Qiu S, Huo B, Dong Y (2021) The ensemble deep learning model for novel COVID-19 on CT images, vol 98","DOI":"10.1016\/j.asoc.2020.106885"},{"issue":"3","key":"2758_CR88","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.1007\/s10489-020-01902-1","volume":"51","author":"R Jain","year":"2021","unstructured":"Jain R, Gupta M, Taneja S, Hemanth DJ (2021) Deep learning based detection and analysis of COVID-19 on chest x-ray images. Appl Intell 51(3):1690\u20131700","journal-title":"Appl Intell"},{"issue":"1","key":"2758_CR89","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s13755-021-00146-8","volume":"9","author":"A Degerli","year":"2021","unstructured":"Degerli A, Ahishali M, Yamac M, Kiranyaz S, Chowdhury MEH, Hameed K, Hamid T, Mazhar R, Gabbouj M (2021) COVID-19 infection map generation and detection from chest x-ray images. Health Inf Sci Syst 9(1):1\u201316","journal-title":"Health Inf Sci Syst"},{"key":"2758_CR90","doi-asserted-by":"publisher","first-page":"102490","DOI":"10.1016\/j.bspc.2021.102490","volume":"66","author":"G Gilanie","year":"2021","unstructured":"Gilanie G, Bajwa UI, Waraich MM, Asghar M, Kousar R, Kashif A, Aslam RS, Qasim MM, Rafique H (2021) Coronavirus (COVID-19) detection from chest radiology images using convolutional neural networks. Biomed Signal Process Control 66:102490","journal-title":"Biomed Signal Process Control"},{"key":"2758_CR91","doi-asserted-by":"publisher","first-page":"103792","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR (2020) Automated detection of COVID-19 cases using deep neural networks with x-ray images. Comput Biol Med 121:103792","journal-title":"Comput Biol Med"},{"key":"2758_CR92","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) Yolo9000: better, faster, stronger. Proc IEEE Conf Comput Vis Pattern Recognit, 7263\u20137271","DOI":"10.1109\/CVPR.2017.690"},{"key":"2758_CR93","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.neucom.2021.03.034","volume":"443","author":"Y Xu","year":"2021","unstructured":"Xu Y, Lam H, Jia G (2021) Manet: a two-stage deep learning method for classification of COVID-19 from chest x-ray images. Neurocomputing 443:96\u2013105","journal-title":"Neurocomputing"},{"key":"2758_CR94","doi-asserted-by":"publisher","first-page":"100041","DOI":"10.1016\/j.bea.2022.100041","volume":"3","author":"R Hertel","year":"2022","unstructured":"Hertel R, Benlamri R (2022) A deep learning segmentation-classification pipeline for x-ray-based COVID-19 diagnosis. Biomed Eng Adv 3:100041","journal-title":"Biomed Eng Adv"},{"issue":"15","key":"2758_CR95","doi-asserted-by":"publisher","first-page":"2296","DOI":"10.3390\/electronics11152296","volume":"11","author":"I Ahmed","year":"2022","unstructured":"Ahmed I, Chehri A, Jeon G (2022) A sustainable deep learning-based framework for automated segmentation of COVID-19 infected regions: using u-net with an attention mechanism and boundary loss function. Electronics 11(15):2296","journal-title":"Electronics"},{"key":"2758_CR96","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention, Springer, Cham, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"2758_CR97","doi-asserted-by":"crossref","unstructured":"Punn NS, Agarwal S (2022) Chs-net: a deep learning approach for hierarchical segmentation of COVID-19 via CT images. Neural Process Lett, 1\u201322","DOI":"10.1007\/s11063-022-10785-x"},{"issue":"6","key":"2758_CR98","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.3390\/s21062215","volume":"21","author":"A Voulodimos","year":"2021","unstructured":"Voulodimos A, Protopapadakis E, Katsamenis I, Doulamis A, Doulamis N (2021) A few-shot u-net deep learning model for COVID-19 infected area segmentation in CT images. Sensors 21(6):2215","journal-title":"Sensors"},{"key":"2758_CR99","doi-asserted-by":"crossref","unstructured":"Cong R, Zhang Y, Yang N, Li H, Zhang X, Li R, Chen Z, Zhao Y, Kwong S (2022) Boundary guided semantic learning for real-time COVID-19 lung infection segmentation system. IEEE Trans Consum Electron","DOI":"10.1109\/TCE.2022.3205376"},{"key":"2758_CR100","unstructured":"Cohen JP, Morrison P, Dao L, Roth K, Duong TQ, Ghassemi M (2020) COVID-19 image data collection: prospective predictions are the future,\u201d arXiv preprint arXiv:2006.11988"},{"key":"2758_CR101","unstructured":"Bganglia (2022) COVID-chestxray-dataset, Available at https:\/\/github.com\/ieee8023\/covid-chestxray-dataset. Accessed 09 Sep 2022"},{"key":"2758_CR102","unstructured":"Larxel (2022) COVID-19 x rays,\u201d Available at https:\/\/www.kaggle.com\/datasets\/andrewmvd\/convid19-x-rays. Accessed 09 Sep 2022"},{"key":"2758_CR103","doi-asserted-by":"publisher","first-page":"132 665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"MEH Chowdhury","year":"2020","unstructured":"Chowdhury MEH, Rahman T, Khandakar A, Mazhar R, Kadir MA, Mahbub ZB, Islam KR, Khan MS, Iqbal A, Al-Emadi N, Reaz MBI, Islam MT (2020) Can AI help in screening viral and COVID-19 pneumonia?. IEEE Access 8:132 665\u2013 132 676","journal-title":"IEEE Access"},{"key":"2758_CR104","doi-asserted-by":"publisher","first-page":"104319","DOI":"10.1016\/j.compbiomed.2021.104319","volume":"132","author":"T Rahman","year":"2021","unstructured":"Rahman T, Khandakar A, Qiblawey Y, Tahir A, Kiranyaz S, Kashem SBA, Islam MT, Al Maadeed S, Zughaier SM, Khan MS, Chowdhury ME (2021) Exploring the effect of image enhancement techniques on COVID-19 detection using chest x-ray images. Comput Biol Med 132:104319","journal-title":"Comput Biol Med"},{"key":"2758_CR105","unstructured":"Rahman T (2022) COVID-19 radiography database, Available at https:\/\/www.kaggle.com\/datasets\/tawsifurrahman\/covid19-radiography-database. Accessed 09 Sep 2022"},{"key":"2758_CR106","unstructured":"Lindawangg (2022) Covidx dataset, Available at https:\/\/github.com\/lindawangg\/COVID-Net\/blob\/master\/docs\/COVIDx.md. Accessed 09 Sep 2022"},{"key":"2758_CR107","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.3389\/fmed.2020.608525","volume":"7","author":"H Gunraj","year":"2020","unstructured":"Gunraj H, Wang L, Wong A (2020) Covidnet-ct: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest CT images. Front Med 7:1025","journal-title":"Front Med"},{"key":"2758_CR108","unstructured":"Gunraj H (2022) Covidx CT, Available at https:\/\/www.kaggle.com\/datasets\/c395fb339f210700ba392d81bf200f766418238c2734e5237b5dd0b6fc724fcb\/versions\/1. Accessed 09 Sep 2022"},{"key":"2758_CR109","unstructured":"Hgunraj (2022) Covidx CT-3,\u201d Available at https:\/\/www.kaggle.com\/datasets\/hgunraj\/covidxct. Accessed 09 Sep 2022"},{"key":"2758_CR110","unstructured":"Zhao J, Zhang Y, He X, Xie P (2020) COVID-CT-dataset: a CT scan dataset about COVID-19,\u201d arXiv preprint arXiv:2003.13865, 490"},{"key":"2758_CR111","unstructured":"Jkooy (2022) COVID-CT, Available at https:\/\/github.com\/UCSD-AI4H\/COVID-CT. Accessed 09 Sep 2022"},{"key":"2758_CR112","unstructured":"NHS (2022) National COVID-19 chest image database (nccid), Available at nhsx.github.io\/covid-chest-imaging-database\/. Accessed 09 September 2022"},{"key":"2758_CR113","unstructured":"Bachir (2022) COVID-19 chest xray, Available at www.kaggle.com\/bachrr\/covid-chest-xray. Accessed 09 Sep 2022"},{"issue":"6","key":"2758_CR114","doi-asserted-by":"publisher","first-page":"1423","DOI":"10.1016\/j.cell.2020.04.045","volume":"181","author":"K Zhang","year":"2020","unstructured":"Zhang K, Liu X, Shen J, Li Z, Sang Y, Wu X, Zha Y, Liang W, Wang C, Wang K, Ye L (2020) Clinically applicable ai system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography. Cell 181(6):1423\u20131433","journal-title":"Cell"},{"key":"2758_CR115","unstructured":"CNCB-NGDC (2022) AI diagnosis, Available at ncov-ai.big.ac.cn\/download?lang=en. Accessed 09 Sep 2022"},{"key":"2758_CR116","unstructured":"Muhammedtalo (2022) COVID-19 x-ray image dataset, Available at github.com\/muhammedtalo\/COVID-19\/tree\/master\/X-Ray. Accessed 09 Sep 2022"},{"key":"2758_CR117","unstructured":"Jannisborn (2022) COVID19_ultrasound, Available at github.com\/jannisborn\/covid19_ultrasound\/tree\/master\/data. Accessed 09 Sep 2022"},{"key":"2758_CR118","unstructured":"D v7 Labs (2022) COVID-19 chest x-ray dataset, Available at https:\/\/darwin.v7labs.com\/v7-labs\/covid-19-chest-x-ray-dataset. Accessed 09 Sep 2022"},{"key":"2758_CR119","unstructured":"Aysendegerli (2022) Qata-cov19 dataset, Available at www.kaggle.com\/aysendegerli\/qatacov19-dataset. Accessed 09 Sep 2022"},{"key":"2758_CR120","unstructured":"AI AS (2022) COVID-19 CT segmentation dataset, Available at https:\/\/medicalsegmentation.com\/covid19\/. Accessed 09 Sep 2022"},{"key":"2758_CR121","unstructured":"Ma J, Ge C, Wang Y, An X, Gao J, Yu Z, Zhang M, Liu X, Deng X, Cao S, Wei H, Mei S, Yang X, Nie Z, Li C, Tian L, Zhu Y, Zhu Q, Dong G, He J (2020) COVID-19 CT lung and infection segmentation dataset, Zenodo"},{"key":"2758_CR122","unstructured":"Zenodo (2022) COVID-19 CT lung and infection segmentation dataset, Available at https:\/\/zenodo.org\/record\/3757476#.YxwGenbMJPa. Accessed 09 Sep 2022"},{"key":"2758_CR123","doi-asserted-by":"crossref","unstructured":"Ma J, Wang Y, An X, Ge C, Yu Z, Chen J, Zhu Q, Dong G, He J, He Z, Nie Z, Yang X (2020) Towards efficient COVID-19 CT annotation: a benchmark for lung and infection segmentation, arXiv e-print: pp.arXiv-2004","DOI":"10.1002\/mp.14676"},{"key":"2758_CR124","unstructured":"Ma J (2022) COVID-19-CT-Seg-Benchmark, Available at https:\/\/gitee.com\/junma11\/COVID-19-CT-Seg-Benchmark\/tree\/master#datasets. Accessed 09 Sep 2022"},{"key":"2758_CR125","unstructured":"GoogleDrive (2022) COVID-SemiSeg, Available at https:\/\/drive.google.com\/file\/d\/1bbKAqUuk7Y1q3xsDSwP07oOXN_GL3SQM\/view. Accessed 09 Sep 2022"},{"key":"2758_CR126","unstructured":"BSTI (2022) COVID-19 imaging database, Available at https:\/\/www.bsti.org.uk\/training-and-education\/covid-19-bsti-imaging-database\/. Accessed 09 Sep 2022"},{"key":"2758_CR127","unstructured":"RAIOSS (2022) Coronacases, Available at coronacases.org\/. Accessed 09 Sep 2022"},{"key":"2758_CR128","unstructured":"ChestImaging (2022) Chest imaging, Available at https:\/\/twitter.com\/chestimaging. Accessed 09 Sep 2022"},{"key":"2758_CR129","unstructured":"SIRM (2022) COVID-19 database, Available at https:\/\/sirm.org\/covid-19\/. Accessed 09 Sep 2022"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02758-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02758-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02758-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T04:21:06Z","timestamp":1683865266000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02758-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,28]]},"references-count":129,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["2758"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02758-y","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,28]]},"assertion":[{"value":"14 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 December 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 January 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}