nach oben

Artificial Intelligence Review

Erschienen in:

03.01.2022

Using artificial intelligence technology to fight COVID-19: a review

verfasst von: Yong Peng, Enbin Liu, Shanbi Peng, Qikun Chen, Dangjian Li, Dianpeng Lian

Erschienen in: Artificial Intelligence Review | Ausgabe 6/2022

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In late December 2019, a new type of coronavirus was discovered, which was later named severe acute respiratory syndrome coronavirus 2(SARS-CoV-2). Since its discovery, the virus has spread globally, with 2,975,875 deaths as of 15 April 2021, and has had a huge impact on our health systems and economy. How to suppress the continued spread of new coronary pneumonia is the main task of many scientists and researchers. The introduction of artificial intelligence technology has provided a huge contribution to the suppression of the new coronavirus. This article discusses the main application of artificial intelligence technology in the suppression of coronavirus from three major aspects of identification, prediction, and development through a large amount of literature research, and puts forward the current main challenges and possible development directions. The results show that it is an effective measure to combine artificial intelligence technology with a variety of new technologies to predict and identify COVID-19 patients.

Vorheriger Artikel Decision support modeling for multiple criteria assessments using a likelihood-based consensus ranking method under Pythagorean fuzzy uncertainty

Nächster Artikel Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Abiyev RH, Ma’aitah MKS (2018) Deep convolutional neural networks for chest diseases detection. J Healthc Eng. https://doi.org/10.1155/2018/4168538CrossRef

Afshar P, Heidarian S, Naderkhani F, Oikonomou A, Plataniotis KN, Mohammadi A (2020) Covid-caps: a capsule networkbased framework for identification of covid-19 cases from x-ray images. Pattern Recogn Lett. https://doi.org/10.1016/j.patrec.2020.09.010CrossRef

Ai T, Yang ZL, Hou HY et al (2020) Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296:E32–E40. https://doi.org/10.1148/radiol.2020200642CrossRef

Albahri OS, Zaidan AA, Albahri AS et al (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. J Infect Public Health 3:1381–1396. https://doi.org/10.1016/j.jiph.2020.06.028CrossRef

Alimadadi A, Aryal S, Manandhar I et al (2020) Artificial intelligence and machine learning to fight COVID-19. Physiol Genomics 52:200–202. https://doi.org/10.1152/physiolgenomics.00029.2020CrossRef

Alqurashi S, Alhindi A, Alanazi E (2020) Large arabic twitter dataset on covid-19. https://arxiv.org/abs/2004.04315

Al-Rousan N, Al-Najjar H (2020) The correlation between the spread of COVID-19 infections and weather variables in 30 Chinese provinces and the impact of Chinese government mitigation plans. Eur Rev Med Pharmacol Sci 24:4565–4571. https://doi.org/10.26355/eurrev_202004_21042CrossRef

Anasir MI, Poh CL (2019) Structural vaccinology for viral vaccine design. Front Microbiol. https://doi.org/10.3389/fmicb.2019.00738CrossRef

Anzai A, Kobayashi T, Linton NM, Kinoshita R, Hayashi K, Suzuki A, Yang Y, Jung S-M, Miyama T, Akhmetzhanov AR et al (2020) Assessing the impact of reduced travel on exportation dynamics of novel coronavirus infection (covid-19). J Clin Med 9(2):601CrossRef

Apostolopoulos ID, Mpesiana TA (2020) Covid-19: automatic detection from x-ray images utilizing transfer learning with convolutional neural networks. Phys Eng Sci Med 43:635CrossRef

Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard AA (2017) Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput Methods Programs Biomed 141:19–26. https://doi.org/10.1016/j.cmpb.2017.01.004CrossRef

Ardakani AA, Kanafi AR, Acharya UR, Khadem N et al (2020) Application of deep learning technique to manage COVID-19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Comput Biol Med 121:103795. https://doi.org/10.1016/j.compbiomed.2020.103795CrossRef

Ashburn TT, Thor KB (2014) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discovery 3:673–683. https://doi.org/10.1038/nrd1468CrossRef

Ayyoubzadeh SM, Ayyoubzadeh SM, Zahedi H et al (2020) Predicting COVID-19 incidence through analysis of google trends data in Iran: data mining and deep learning pilot study. JMIR Public Health Surveill 6:e18828. https://doi.org/10.2196/18828CrossRef

Azevedo MA (2020) Hospital-disinfecting robots: xenex sees surge in orders as COVID-19 pandemic escalates. Crunchbase News. https://news.crunchbase.com/news/hospital-disinfecting-robots-xenex-sees-surge-in-orders-as-covid-19-pandemic-escalates/. Accessed 26 Oct 2020

Banda JM, Tekumalla R, Wang G, Yu J, Liu T, Ding Y, Artemova E, Tutubalina E, Chowell G (2021) A large-scale covid-19 twitter chatter dataset for open scientific research – an international collaboration. Epidemiologia 2:315–324. https://doi.org/10.3390/epidemiologia2030024CrossRef

Bansal A, Padappayil R, Garg C et al (2020) Utility of artificial intelligence amidst the COVID 19 pandemic: a review. J Med Syst 44:156. https://doi.org/10.1007/s10916-020-01617-3CrossRef

Bastawrous A, Armstrong MJ (2013) Mobile health use in low- and high-income countries: an overview of the peer-reviewed literature. R Soc Med J 106:130–142. https://doi.org/10.1177/0141076812472620CrossRef

Baz M, Khatri S, Baz A, Alhakami H, Agrawal A, Khan RA (2022) Blockchain and artificial intelligence applications to defeat COVID-19 pandemic. Comput Syst Sci Eng 40(2):691–702. https://doi.org/10.32604/csse.2022.019079CrossRef

Belfiore MP, Urraro F, Grassi R et al (2020) Artificial intelligence to codify lung CT in Covid-19 patients. Radiol Med 125:500–504. https://doi.org/10.1007/s11547-020-01195-xCrossRef

Bonnie C (2020) The influence of the COVID-19 pandemic on technology: adoption in health care. Nurse Lead 18:500–503. https://doi.org/10.1016/j.mnl.2020.06.008CrossRef

Bragazzi NL, Ricco M, Pacifico A, Malagoli P, Kridin K, Pigatto P, Damiani G (2020a) COVID-19 knowledge prevents biologics discontinuation: data from an Italian multicenter survey during RED-ZONE declaration. Dermatol Ther. https://doi.org/10.1111/dth.13508CrossRef

Bragazzi NL, Dai HJ, Damiani G et al (2020b) How big data and artificial intelligence can help better manage the COVID-19 pandemic. Int J Environ Res Public Health. https://doi.org/10.3390/ijerph17093176CrossRef

Brunese L, Mercaldo F, Reginelli A, Santone A (2020) Explainable deep learning for pulmonary disease and coronavirus COVID-19 detection from X-rays. Comput Methods Programs Biomed 196:105608. https://doi.org/10.1016/j.cmpb.2020.105608CrossRef

Caramelo F, Ferreira N, Oliveiros B (2020) Estimation of risk factors for COVID-19 mortality—preliminary results. Medrxiv. https://doi.org/10.1101/2020.02.24.20027268CrossRef

Chan JFW, Yuan SF, Kok KH et al (2020) A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 395:514–523. https://doi.org/10.1016/S0140-6736(20)30154-9CrossRef

Chen DG, Chen XG, Chen JK (2020a) Reconstructing and forecasting the COVID-19 epidemic in the United States using a 5-parameter logistic growth model. Global Health Res Policy 5:25. https://doi.org/10.1186/s41256-020-00152-5CrossRef

Chen E, Lerman K, Ferrara E (2020b) Covid-19: the first public coronavirus twitter dataset. https://arxiv.org/abs/2003.07372

Chimmula VKR, Zhang L (2020) Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos Solitons Fractals. https://doi.org/10.1016/j.chaos.2020.109864CrossRefMATH

Choi S, Lee J, Kang MG et al (2017) Large-scale machine learning of media outlets for understanding public reactions to nation-wide viral infection outbreaks. Methods 129:50–59. https://doi.org/10.1016/j.ymeth.2017.07.027CrossRef

Clipper B, Batcheller J, Thomaz AL, Rozga A (2019) Artificial intelligence and robotics: a nurse leader’s primer. Nurse Lead 16:379–384. https://doi.org/10.1016/j.mnl.2018.07.015CrossRef

Cohen JP, Morrison P, Dao I (2020) Covid-19 image data collection. https://arxiv.org/abs/2003.11597

Coronavirus Resource Center (2020) https://coronavirus.jhu.edu/map.html. Accessed 9 Oct 2020

Coronavirus Resource Center (2021) https://coronavirus.jhu.edu/map.html. Accessed 15 Jan 2021

Cristaudo A, Pigliacelli F, Pacifico A, Damiani G, Iacovelli P, Morrone A (2020) Teledermatology and hygiene practices during theCOVID-19 pandemic. Contact Dermatitis 86(6):536–536. https://doi.org/10.1111/cod.13683CrossRef

Cyclica (2020) China’s Institute of Materia Medica Partners with Cyclica on Innovative drug repurposing for COVID-19. Business Wire

Damiani G, Gironi LC, Kridin K et al (2021a) Mask-induced Koebner phenomenon and its clinical phenotypes: a multicenter, real-life study focusing on 873 dermatological consultations during COVID-19 pandemics. Dermatol Ther. https://doi.org/10.1111/dth.14823CrossRef

Damiani G, Gironi LC, Grada A et al (2021b) COVID-19 related masks increase severity of both acne (maskne) and rosacea (mask rosacea): multi-center, real-life, telemedical, and observational prospective study. Dermatol Ther. https://doi.org/10.1111/dth.14848CrossRef

Damiani G, Gironi LC, Pacifico A, Cristaudo A, Malagoli P, Allocco F, Bragazzi NL, Linder DM, Santus P, Buja A (2021c) Masks use and facial dermatitis during COVID-19 outbreak: is there a difference between CE and non-CE approved masks? Multi-center, real-life data from a large Italian cohort. G Ital Dermatol Venereol 156(2):220–225. https://doi.org/10.23736/S2784-8671.21.06895-4CrossRef

Dean Ho (2020) Addressing COVID-19 drug development with artificial intelligence. Adv Intell Syst (Weinheim an Der Bergstrasse, Germany). https://doi.org/10.1002/aisy.202000070CrossRef

Deng CX (2020) The global battle against SARS-CoV-2 and COVID-19. Int J Biol Sci 16:1676–1677. https://doi.org/10.7150/ijbs.45587CrossRef

Dong E, Du H, Gardner L (2020) An interactive web-based dashboard to track covid-19 in real time. Lancet Infect Dis 20(5):533–534. https://doi.org/10.1016/S1473-3099(20)30120-1CrossRef

Du Z, Xu X, Wu Y, Wang L, Cowling BJ, Meyers LA (2020) The serial interval of covid-19 from publicly reported confirmed cases. medRxiv: the preprint server for health sciences. https://doi.org/10.1101/2020.02.19.20025452

Ekert JE, Deakyne J, Pribul-Allen P et al (2020) Recommended guidelines for developing, qualifying, and implementing complex in vitro models (CIVMs) for drug discovery. Slas Discovery. https://doi.org/10.1177/2472555220923332CrossRef

Fan DP, Zhou T, Ji GP et al (2020) Inf-Net: automatic COVID-19 lung infection segmentation from CT images. IEEE Trans Med Imaging 39:2626–2637. https://doi.org/10.1109/TMI.2020.2996645CrossRef

Fraga AG, Barbosa AM, Ferreira CM et al (2018) Immune-evasion strate-gies of mycobacteria and their implications for the protective immune response. Curr Issues Mol Biol 25:169–198. https://doi.org/10.21775/cimb.025.169CrossRef

Garg L, Chukwu E, Nasser N, Chakraborty C, Garg G (2020) Anonymity preserving IoT-based COVID-19 and other infectious disease contact tracing model. IEEE Access 8:159402–159414. https://doi.org/10.1109/ACCESS.2020.3020513CrossRef

Ge Y, Tian T, Huang S et al (2020) A data-driven drug repositioning framework discovered a potential therapeutic agent targeting COVID-19. Biorxiv 3. https://doi.org/10.1101/2020.03.11.986836CrossRef

Giamberardino PD, Iacoviello D (2020) Epidemic modeling and control of HIV/AIDS dynamics in populations under external interactions: a worldwide challenge. Control Appl Biomed Eng Syst. https://doi.org/10.1016/B978-0-12-817461-6.00008-1CrossRef

Gironi LC, Damiani G, Zavattaro E et al (2021) Tetracyclines in COVID-19 patients quarantined at home: Literature evidence supporting real-world data from a multicenter observational study targeting inflammatory and infectious dermatoses. Dermatol Ther. https://doi.org/10.1111/dth.14694CrossRef

Gordon DE, Jang GM, Bouhaddou M, Xu J, Obernier K et al (2020) A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature 583:459–468. https://doi.org/10.1038/s41586-020-2286-9CrossRef

Guan WJ, Liang WH, Zhao Y et al (2020) Comorbidity and its impact on 1,590 patients with COVID-19 in China: a nationwide analysis. Eur Respir J. https://doi.org/10.1183/13993003.00547-2020CrossRef

Han ZY, Wei BZ, Hong YF et al (2020) Accurate screening of COVID-19 using attention-based deep 3D multiple instance learning. IEEE Trans Med Imaging 39:2584–2594. https://doi.org/10.1109/TMI.2020.2996256CrossRef

Harrison X, Wang R, Xiong Z et al (2020) Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT. Radiology 269:E156–E165. https://doi.org/10.1148/radiol.2020201491CrossRef

Hong L, Lin J, Tao J, Zeng J (2019) BERE: an accurate distantly supervised biomedical entity relation extraction network, arXiv preprint arXiv, 1906.06916

Huang B, Ling R, Cheng Y et al (2020a) Characteristics of the coronavirus disease 2019 and related therapeutic options. Mol Ther Methods Clin Dev 18:367–375. https://doi.org/10.1016/j.omtm.2020.06.013CrossRef

Huang Z, Tian D, Liu Y et al (2020b) Ultra-sensitive and high-throughput CRISPR-p owered COVID-19 diagnosis. Biosens Bioelectron. https://doi.org/10.1016/j.bios.2020.112316CrossRef

Hultstrom M, von Seth M, Frithiof R (2020) Hyperreninemia and low total body water may contribute to acute kidney injury in COVID-19 patients in intensive care. J Hypertens 38:1613–1614. https://doi.org/10.1097/HJH.0000000000002531CrossRef

Jabarulla MY, Lee HN (2021) A blockchain and artificial intelligence-based, patient-centric healthcare system for combating the COVID-19 pandemic: opportunities and applications. Healthcare. https://doi.org/10.3390/healthcare9081019CrossRef

Jalaber C, Lapotre T, Morcet-Delattre T et al (2020) Chest CT in COVID-19 pneumonia: a review of current knowledge. Diagn Interv Imaging 101:431–437. https://doi.org/10.1016/j.diii.2020.06.001CrossRef

Jun M, Cheng G, Yixin W et al (2020) COVID-19 CT lung and infection segmentation dataset. https://zenodo.org/record/3757476

Kaufmann SHE, Juliana M, Lewis DJM, Giuseppe D (2014) Challenges and responses in human vaccine development. Curr Opin Immunol 28:18–26. https://doi.org/10.1016/j.coi.2014.01.009CrossRef

Ke YY, Peng TT, Yeh TK et al (2020) Artificial intelligence approach fighting COVID-19 with repurposing drugs. Biomed J. https://doi.org/10.1016/j.bj.2020.05.001CrossRef

Khan A, Shah J, Bhat M (2020) CoroNet: a deep neural network for detection and diagnosis of COVID-19 from chest X-ray images. Comput Methods Programs Biomed 196:105581. https://doi.org/10.1016/j.cmpb.2020.105581CrossRef

Killeen BD, Wu JY, Shah K, Zapaishchykova A, Nikutta P, Tamhane A, Chakraborty S, Wei J, Gao T, Thies M et al (2020) A county-level dataset for informing the United States’ response to covid-19. https://arxiv.org/abs/2004.00756

Kleinberg B, van der V egt I, Mozes M (2020) Measuring emotions in the covid-19 real world worry dataset. https://arxiv.org/abs/2004.04225

Kraemer MUG, Yang C-H, Gutierrez B, Wu C-H, Klein B, Pigott DM, du Plessis L, Faria NR, Li R, Hanage WP et al (2020) The effect of human mobility and control measures on the covid-19 epidemic in China. medRxiv: the preprint server for health sciences. https://doi.org/10.1101/2020.03.02.20026708

Krishna K, Narendra K, Rachna S (2020) Role of IoT to avoid spreading of COVID-19. Int J Intell Netw 1:32–35. https://doi.org/10.1016/j.ijin.2020.05.002CrossRef

Kryshtafovych A, Schwede T, Topf M et al (2019) Critical assessment of methods of protein structure prediction (CASP)-Round XIII. Proteins-Struct Funct Bioinform 87:1011–1020. https://doi.org/10.1002/prot.25823CrossRef

Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, Eggo RM, Sun F, Jit M, Munday JD et al (2020) Early dynamics of transmission and control of covid-19: a mathematical modelling study. Lancet Infect Dis. https://doi.org/10.1016/S1473-3099(20)30144-4CrossRef

Kumar A, Gupta PK, Srivastava A (2020) A review of modern technologies for tackling COVID-19 pandemic. Diabetes Metab Syndr Clin Res Rev 4:569–573. https://doi.org/10.1016/j.dsx.2020.05.008CrossRef

Kumar A, Sharma K, Singh H et al (2021) A drone-based networked system and methods for combating coronavirus disease (COVID-19) pandemic. Futur Gener Comput Syst 115:1–19. https://doi.org/10.1016/j.future.2020.08.046CrossRef

Li L, Qin L, Xu Z et al (2020) Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 269:E65–E67. https://doi.org/10.1148/radiol.2020200905CrossRef

Liang H, Tsui BY, Ni H et al (2019) Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med 25:433–438. https://doi.org/10.1038/s41591-018-0335-9CrossRef

Liang GS, Fan WG, Luo H, Zhu X (2020) The emerging roles of artificial intelligence in cancer drug development and precision therapy. Biomed Pharmacother 128:110255. https://doi.org/10.1016/j.biopha.2020.110255CrossRef

Liestol K, Andersen PK (2002) Updating of covariates and choice of time origin in survival analysis: problems with vaguely defined disease states. Stat Med 21:3701–3714. https://doi.org/10.1002/sim.1322CrossRef

Liu W, Yen PT-W, Cheong SA (2020) Coronavirus disease 2019 (covid-19) outbreak in china, spatial temporal dataset. https://arxiv.org/abs/2003.11716

Mak K, Pichika M (2019) Artificial intelligence in drug development: present status and future prospects. Drug Discov Today 24:773–780. https://doi.org/10.1016/j.drudis.2018.11.014CrossRef

Maleki M, Mahmoudi MR, Wraith D, Pho K (2020) Time series modelling to forecast the confirmed and recovered cases of COVID-19. Travel Med Infect Dis. https://doi.org/10.1016/j.tmaid.2020.101742CrossRef

Mashamba-Thompson TP, Crayton ED (2020) Blockchain and artificial intelligence technology for novel coronavirus disease 2019 self-testing. Diagnostics. https://doi.org/10.3390/diagnostics10040198CrossRef

McCall B (2020) COVID-19 and artificial intelligence: protecting health-care workers and curbing the spread. Lancet Digit Health 2:e166–e167. https://doi.org/10.1016/S2589-7500(20)30054-6CrossRef

McKibbin W, Fernando R (2020) The global macroeconomic impacts of COVID-19: seven scenarios. SSRN Electron J. https://doi.org/10.2139/ssrn.3547729CrossRef

Mohamed L, Florentin S, Khalifa M (2020a) Within the lack of chest COVID-19 X-ray dataset: a novel detection model based on GAN and deep transfer learning. Symmetry 12:651. https://doi.org/10.3390/sym12040651CrossRef

Mohamed AB, Mohamed R, Mohamed E et al (2020b) A model for the effective COVID-19 identification in uncertainty environment using primary symptoms and CT scans. Health Inform J. https://doi.org/10.1177/1460458220952918CrossRef

Mutlu Ece C, Toktam O, Jasser J et al (2020) A stance data set on polarized conversations on Twitter about the efficacy of hydroxychloroquine as a treatment for COVID-19. Data Brief 33:106401. https://doi.org/10.1016/j.dib.2020.106401CrossRef

NazSindhu T, Shafiq A, Al-Mdallal QM (2021) Exponentiated transformation of Gumbel Type-II distribution for modeling COVID-19 data. Alex Eng J 60(1):671–689. https://doi.org/10.1016/j.aej.2020.09.060CrossRef

Neill DB (2013) Using artificial intelligence to improve hospital inpatient care. IEEE Intell Syst 28(2):92–95. https://doi.org/10.1109/MIS.2013.51MathSciNetCrossRef

Nishiura H, Linton NM, Akhmetzhanov AR (2020) Serial interval of novel coronavirus (covid-19) infections. Int J Infect Dis 93:284–286. https://doi.org/10.1016/j.ijid.2020.02.060CrossRef

Ozdemir V (2020) Special issue: digital health in times of COVID-19. OMICS J Integr Biol 24:229–230. https://doi.org/10.1089/omi.2020.0049CrossRef

Ozturk T, Talo M, Yildirim E et al (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med. https://doi.org/10.1016/j.compbiomed.2020.103792CrossRef

Pan F, Ye TH, Sun P et al (2020) Time course of lung changes a chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology 295(3):715–721. https://doi.org/10.1148/radiol.2020200370CrossRef

Paolotti D, Carnahan A, Colizza V et al (2014) Web-based participatory surveillance of infectious diseases: the influenzanet participatory surveillance experience. Clin Microbiol Infect 20:17–21. https://doi.org/10.1111/1469-0691.12477CrossRef

Park Y, Casey D, Joshi I, Zhu J, Cheng F (2020) Emergence of new disease: how can artificial intelligence help? Trends Mol Med 26:627–629. https://doi.org/10.1016/j.molmed.2020.04.007CrossRef

Patel BN, Rosenberg L, Willcox G et al (2019) Human-machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digit Med 2:111. https://doi.org/10.1038/s41746-019-0189-7CrossRef

Pathak Y, Shukla PK, Tiwari A et al (2020) Deep transfer learning based classification model for COVID-19 disease. IRBM. https://doi.org/10.1016/j.irbm.2020.05.003CrossRef

Pham QV, Nguyen DC, Thien HT, Hwang WJ, Pathirana PN (2020) Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: a survey on the state-of-the-arts. IEEE Access 8:130820–130839. https://doi.org/10.1109/ACCESS.2020.3009328CrossRef

Pirouz B, Haghshenas SS, Haghshenas SS et al (2020) Investigating a serious challenge in the sustainable development process: analysis of confirmed cases of COVID-19 (New Type of Coronavirus) through a binary classification using artificial intelligence and regression analysis. Sustainability 12:2427. https://doi.org/10.3390/su12062427CrossRef

Pu X, Chen K, Liu J et al (2020) Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19. J Biomed Eng 37:365–372. https://doi.org/10.7507/1001-5515.202003045CrossRef

Pushpakom S, Iorio F, Eyers PA, Escott KJ, Hopper S, Wells A et al (2019) Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discovery 3:41–58. https://doi.org/10.1038/nrd.2018.168CrossRef

Qiu JY, Zhou DS, Liu J, Yuan TF (2020) Mental wellness system for COVID-19. Brain Behav Immun 87:51–52. https://doi.org/10.1016/j.bbi.2020.04.032CrossRef

Rahimzadeh M, Attar A (2020) A modified deep convolutional neural network for detecting COVID-19 and pneumonia from chest X-ray images based on the concatenation of Xception and ResNet50V2. Inform Med Unlocked 19:100360. https://doi.org/10.1016/j.imu.2020.100360CrossRef

Rajaraman S, Antani S (2020) Weakly labeled data augmentation for deep learning: a study on COVID-19 detection in chest X-Rays. Diagnostics 10:E358. https://doi.org/10.3390/diagnostics10060358CrossRef

Rao ASRS, Diamond MP (2020) Deep learning of Markov model-based machines for determination of better treatment option decisions for infertile women. Reprod Sci 27(2):763–770. https://doi.org/10.1007/s43032-019-00082-9CrossRef

Rao ASRS, Vazquez JA (2020) Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine. Infect Control Hosp Epidemiol 41(7):826–830. https://doi.org/10.1017/ice.2020.61CrossRef

Rasheed J, Jamil A, Hameed AA et al (2020) A survey on artificial intelligence approaches in supporting frontline workers and decision makers for the COVID-19 pandemic. Chaos Solitons Fractals 141:110337. https://doi.org/10.1016/j.chaos.2020.110337CrossRefMATH

Renu K, Prasanna PL, Valsala Gopalakrishnan A (2020) Coronaviruses pathogenesis, comorbidities and multi-organ damage—a review. Life Sci. https://doi.org/10.1016/j.lfs.2020.117839CrossRef

Muqattash R, Niankara I, Traoret RI (2020) Survey data for COVID-19 vaccine preference analysis in the United Arab Emirates. Data Brief 33:106446. https://doi.org/10.1016/j.dib.2020.106446CrossRef

Rubin GD, Ryerson CJ, Haramati LB et al (2020) The role of chest imaging in patient management during the COVID-19 pandemic a multinational consensus statement from the fleischner society. Chest 158:106–116. https://doi.org/10.1016/j.chest.2020.04.003CrossRef

Russo G, Reche P, Pennisi M, Pappalardo F (2020) The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opin Drug Discov. https://doi.org/10.1080/17460441.2020.1791076CrossRef

Ryan L, Lam C, Mataraso S et al (2020) Mortality prediction model for the triage of COVID-19, pneumonia, and mechanically ventilated ICU patients: a retrospective study. Ann Med Surg 59:207–216. https://doi.org/10.1016/j.amsu.2020.09.044CrossRef

Schaarschmidt J, Monastyrskyy B, Kryshtafovych A et al (2018) Assessment of contact predictions in CASP12: co-evolution and deep learning coming of age. Proteins-Struct Funct Bioinform 86:51–66. https://doi.org/10.1002/prot.25407CrossRef

Scudellari M (2020) Five companies using AI to fight coronavirus. IEEE Spectrum 2020. https://spectrum.ieee.org/the-human-os/artificial-intelligence/medical-ai/companies-ai-coronavirus. Accessed 20 Oct 2020

Senior AW, Evans R, Jumper J et al (2020b) Improved protein structure prediction using potentials from deep learning. Nature 577:706–710. https://doi.org/10.1038/s41586-019-1923-7CrossRef

Senior A, Jumper J, Hassabis D (2020) AlphaFold: using AI for scientific discovery. Blog post Research. https://deepmind.com/blog/article/alphafold-casp13. Accessed 9 Aug 2020

Shen CH, Chen AF, Luo C et al (2020) Using reports of symptoms and diagnoses on social media to predict COVID-19 case counts in Mainland China: observational infoveillance study. J Med Internet Res 22:e19421. https://doi.org/10.2196/19421CrossRef

Shi F, Wang J, Shi J et al (2020) Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19. IEEE Rev Biomed Eng. https://doi.org/10.1109/RBME.2020.2987975CrossRef

Simoes MF, Silva G, Pinto AC et al (2020) Artificial neural networks applied to quality-by-design: from formulation development to clinical outcome. Eur J Pharm Biopharm 152:282–295. https://doi.org/10.1016/j.ejpb.2020.05.012CrossRef

Singh D, Kumar V, Vaishali KM (2020) Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. Eur J Clin Microbiol Infect Dis 39:1379–1389. https://doi.org/10.1007/s10096-020-03901-zCrossRef

Smith L (2020) How robots helped protect doctors from COVID-19. Fast Company. https://www.fastcompany.com/90476758/how-robots-helped-protect-doctors-from-COVID-19. Accessed 26 Oct 2020

Stephen O, Sain M, Maduh U et al (2019) An efficient deep learning approach to pneumonia classification in healthcare. J Healthc Eng. https://doi.org/10.1155/2019/4180949CrossRef

Tayarani-N M (2020) Applications of artificial intelligence in battling against Covid-19: a literature review. Chaos Solitons Fractals. https://doi.org/10.1016/j.chaos.2020.110338MathSciNetCrossRefMATH

Thakur A, Mikkelsen H, Jungersen G (2019) Intracellular pathogens: host immunity and microbial persistence strategies. J Immunol Res. https://doi.org/10.1155/2019/1356540CrossRef

Timeline of WHO’s response to COVID-19 (2020) https://www.who.int/news-room/detail/29-06-2020-covidtimeline. Accessed 9 Aug 2020

Tiwari S, Kumar S, Guleria K (2020) Outbreak trends of coronavirus disease-2019 in India: a prediction. Disaster Med Public Health Prep 4:1–6. https://doi.org/10.1017/dmp.2020.115CrossRef

Tobias A (2020) Evaluation of the lockdowns for the SARS-CoV-2 epidemic in Italy and Spain after one month follow up. Sci Total Environ. https://doi.org/10.1016/j.scitotenv.2020.138539CrossRef

Togacar M, Ergen B, Comert 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. https://doi.org/10.1016/j.compbiomed.2020.103805CrossRef

Tomar A, Gupta N (2020) Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Sci Total Environ 728:138762. https://doi.org/10.1016/j.scitotenv.2020.138762CrossRef

Tuli S, Tuli S, Tuli R, Gill S (2020a) Predicting the growth and trend of COVID-19 pandemic using machine learning and cloud computing. Internet Things. https://doi.org/10.1016/j.iot.2020.100222CrossRef

Tuli S, Tuli S, Wander G et al (2020b) Next generation technologies for smart healthcare: challenges, vision, model, trends and future directions. Internet Technol Lett. https://doi.org/10.1002/itl2.145CrossRef

Tuncer T, Dogan S, Ozyurt F (2020) An automated residual exemplar local binary pattern and iterative ReliefF based corona detection method using lung X-ray image. Chemom Intell Lab Syst. https://doi.org/10.1016/j.chemolab.2020.104054CrossRef

Ucar F, Korkmaz D (2019) COVIDiagnosis-Net: deep Bayes-SqueezeNet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images. Med Hypotheses 140:109761. https://doi.org/10.1016/j.mehy.2020.109761CrossRef

Vaid S, Kalantar R, Bhandari M (2020) Deep learning COVID-19 detection bias: accuracy through artificial intelligence. Int Orthop 44:1539–1542. https://doi.org/10.1007/s00264-020-04609-7CrossRef

Vaishya R, Javaid M, Khan IH, Haleem A (2020) Artificial Intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab Syndr 14:337–339. https://doi.org/10.1016/j.dsx.2020.04.012CrossRef

Wang XG, Deng XB, Fu Q et al (2020a) A weakly-supervised framework for COVID-19 classification and lesion localization from chest CT. IEEE Trans Med Imaging 39:2615–2625. https://doi.org/10.1109/TMI.2020.2995965CrossRef

Wang SX, Ding SZ, Xiong L (2020b) A new system for surveillance and digital contact tracing for COVID-19: spatiotemporal reporting over network and GPS. J Med Internet Res 8(6):e19457. https://doi.org/10.2196/19457CrossRef

Wang L, Wong A (2020) Covid-net:, A tailored deep convolutional neural network design for detection of covid-19 cases from chest radiography images. https://arxiv.org/abs/2003.09871

Wang S, Kang B, Ma, J et al (2020) A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19). medRxiv

Wells CR, Sah P, Moghadas SM, Pandey A, Shoukat A, Wang Y, Wang Z, Meyers LA, Singer BH, Galvani AP (2020) Impact of international travel and border control measures on the global spread of the novel 2019 coronavirus outbreak. Proc Nat Acad Sci 117(13):7504–7509. https://doi.org/10.1073/pnas.2002616117CrossRef

Xie J, Hungerford D, Chen H et al (2020) Development and external validation of a prognostic multivariable model on admission for hospitalized patients with COVID-19. Medrxiv. https://doi.org/10.1101/2020.03.28.20045997CrossRef

Xu B, Gutierrez B, Mekaru S, Sewalk K, Goodwin L, Loskill A, Cohn EL, Hswen Y, Hill SC, Cobo MM et al (2020) Epidemiological data from the covid-19 outbreak, real-time case information. Sci Data 7(1):1–6. https://doi.org/10.1038/s41597-020-0448-0CrossRef

Xue HQ, Li J, Xie HZ, Wang YD (2018) Review of drug repositioning approaches and resources. Int J Biol Sci 14:1232–1244. https://doi.org/10.7150/ijbs.24612CrossRef

Yan L, Zhang H, Goncalves J et al (2020a) A machine learning-based model for survival prediction in patients with severe COVID-19 infection. Medrxiv. https://doi.org/10.1101/2020.02.27.20028027CrossRef

Yan T, Wong PK, Ren H, Wang H, Wang J, Li Y (2020b) Automatic distinction between COVID-19 and common pneumonia using multi-scale convolutional neural network on chest CT scans. Chaos Solitons Fractals 140:110153. https://doi.org/10.1016/j.chaos.2020.110153MathSciNetCrossRefMATH

Yang Z, Zeng Z, Wang K et al (2020a) Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions. JTD 12:165–168. https://doi.org/10.21037/jt.2020.02.64CrossRef

Yang X, He X, Zhao J et al (2020b) COVID-CT-Dataset: a CT scan dataset about COVID-19. https://arxiv.org/abs/2003.13865

Yanisky-Ravid S, Jin R (2020) Summoning a new artificial intelligence patent model: in the age of pandemic. SSRN. https://doi.org/10.2139/ssrn.3619069CrossRef

Yaqoob S, Khan MM, Talib R (2019) Use of blockchain in healthcare: a systematic literature review. Int J Adv Comput Sci Appl 10(5):644–653. https://doi.org/10.14569/ijacsa.2019.0100581CrossRef

Yu ZK, Li XH, Sun HT et al (2020) Rapid identification of COVID-19 severity in CT scans through classification of deep features. Biomed Eng Online. https://doi.org/10.1186/s12938-020-00807-xCrossRef

Yu J (2020) Open access institutional and news media tweet dataset for covid-19 social science research. https://arxiv.org/abs/2004.01791

Zhang HT, Zhang JS, Zhang HH et al (2020a) Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software. Eur J Nucl Med Mol Imaging. https://doi.org/10.1007/s00259-020-04953-1CrossRef

Zhang Y, Wang C, Han M et al (2020b) Discrimination of false negative results in RT-PCR detection of SARS-CoV-2 RNAs in clinical specimens by using an internal reference. Virol Sin. https://doi.org/10.1007/s12250-020-00273-8CrossRef

Zhao J, Zhang Y, He X, Xie P (2020) Covid-ct-dataset: a CT scan dataset about covid-19. https://arxiv.org/abs/2003.13865

Zhavoronkov A (2018) Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. Mol Pharm 15:4311–4313. https://doi.org/10.1021/acs.molpharmaceut.8b00930CrossRef

Zhavoronkov A (2020) Medicinal chemists versus machines challenge: what will it take to adopt and advance artificial intelligence for drug discovery? J Chem Inf Model 60(6):2657–2659. https://doi.org/10.1021/acs.jcim.0c00435CrossRef

Zheng NN, Du SY, Wang JJ et al (2020) Predicting COVID-19 in China using hybrid AI model. IEEE Trans Cybern 50:2891–2904. https://doi.org/10.1109/TCYB.2020.2990162CrossRef

Zhou Y, Hou Y, Shen J, Huang Y, Martin W, Cheng F (2020) Network-based drug repurposing for novel coronavirus 2019-nCoV/SARS-CoV-2. Cell Discov 6:14. https://doi.org/10.1038/s41421-020-0153-3CrossRef

Zhu NA, Zhang DC, Wang WA et al (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382:727–733. https://doi.org/10.1056/NEJMoa2001017CrossRef

Titel: Using artificial intelligence technology to fight COVID-19: a review
verfasst von: Yong Peng
Enbin Liu
Shanbi Peng
Qikun Chen
Dangjian Li
Dianpeng Lian
Publikationsdatum: 03.01.2022
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 6/2022
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-021-10106-z

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 6/2022

A novel improved whale optimization algorithm to solve numerical optimization and real-world applications

A survey paper on design and implementation of multipliers for digital system applications

A comparison of vector symbolic architectures

Fuzzy covering-based rough set on two different universes and its application

Decision support modeling for multiple criteria assessments using a likelihood-based consensus ranking method under Pythagorean fuzzy uncertainty

Adversarial example detection for DNN models: a review and experimental comparison

Premium Partner