{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:44:58Z","timestamp":1781631898999,"version":"3.54.5"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"20","license":[{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T00:00:00Z","timestamp":1646092800000},"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":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2023,7]]},"DOI":"10.1007\/s00521-022-07055-1","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T17:03:13Z","timestamp":1646154193000},"page":"14591-14609","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["AI-enabled radiologist in the loop: novel AI-based framework to augment radiologist performance for COVID-19 chest CT medical image annotation and classification from pneumonia"],"prefix":"10.1007","volume":"35","author":[{"given":"Hemant","family":"Ghayvat","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Awais","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A. K.","family":"Bashir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharnil","family":"Pandya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohd","family":"Zuhair","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8302-4571","authenticated-orcid":false,"given":"Mamoon","family":"Rashid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jamel","family":"Nebhen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,1]]},"reference":[{"key":"7055_CR1","unstructured":"Aylward B, Liang W (2020) Report of the WHO-china joint mission on coronavirus disease 2019 (COVID-19)"},{"key":"7055_CR2","unstructured":"WHO (2020) Coronavirus disease 2019 (COVID-19) situation Report-24"},{"key":"7055_CR3","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1186\/1475-925X-11-26","volume":"11","author":"S Hwang","year":"2012","unstructured":"Hwang S, Chung G, Lee J et al (2012) Sleep\/wake estimation using only anterior tibialis electromyography data. Biomed Eng Online 11:26. https:\/\/doi.org\/10.1186\/1475-925X-11-26","journal-title":"Biomed Eng Online"},{"key":"7055_CR4","doi-asserted-by":"publisher","first-page":"572","DOI":"10.1136\/emermed-2020-210098","volume":"37","author":"S Carley","year":"2020","unstructured":"Carley S, Horner D, Body R, Mackway-Jones K (2020) Evidence-based medicine and COVID-19: what to believe and when to change. Emerg Med J 37:572\u2013575. https:\/\/doi.org\/10.1136\/emermed-2020-210098","journal-title":"Emerg Med J"},{"key":"7055_CR5","unstructured":"WHO (2020) Laboratory testing strategy recommendations for COVID-19: interim guidance, 21 Mar 2020"},{"key":"7055_CR6","doi-asserted-by":"publisher","DOI":"10.14744\/ejmo.2020.90853","author":"S Ahmad","year":"2020","unstructured":"Ahmad S (2020) A review of COVID-19 (Coronavirus Disease-2019) diagnosis, treatments and prevention. Eurasian J Med Oncol. https:\/\/doi.org\/10.14744\/ejmo.2020.90853","journal-title":"Eurasian J Med Oncol"},{"key":"7055_CR7","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.clinimag.2020.04.001","volume":"64","author":"A Jacobi","year":"2020","unstructured":"Jacobi A, Chung M, Bernheim A, Eber C (2020) Portable chest X-ray in coronavirus disease-19 (COVID-19): a pictorial review. Clin Imaging 64:35\u201342. https:\/\/doi.org\/10.1016\/j.clinimag.2020.04.001","journal-title":"Clin Imaging"},{"key":"7055_CR8","unstructured":"Zhang J, Xie Y, Li Y, Shen C, Xia Y (2020) Covid-19 screening on chest x-ray images using deep learning based anomaly detection. arXiv preprint arXiv:2003.12338"},{"key":"7055_CR9","first-page":"242","volume":"42","author":"O Ozdemir","year":"2020","unstructured":"Ozdemir O (2020) Coronavirus disease 2019 (COVID-19): diagnosis and management. Erciyes Med J 42:242\u2013248","journal-title":"Erciyes Med J"},{"key":"7055_CR10","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m1808","author":"J Watson","year":"2020","unstructured":"Watson J, Whiting PF, Brush JE (2020) Interpreting a covid-19 test result. BMJ m1808. https:\/\/doi.org\/10.1136\/bmj.m1808","journal-title":"BMJ m1808"},{"key":"7055_CR11","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1007\/s00146-020-00978-0","volume":"35","author":"W Naud\u00e9","year":"2020","unstructured":"Naud\u00e9 W (2020) Artificial intelligence vs COVID-19: limitations, constraints and pitfalls. AI Soc 35:761\u2013765. https:\/\/doi.org\/10.1007\/s00146-020-00978-0","journal-title":"AI Soc"},{"key":"7055_CR12","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.jrid.2020.04.003","volume":"7","author":"H Chen","year":"2020","unstructured":"Chen H, Ai L, Lu H, Li H (2020) Clinical and imaging features of COVID-19. Radiol Infect Dis 7:43\u201350. https:\/\/doi.org\/10.1016\/j.jrid.2020.04.003","journal-title":"Radiol Infect Dis"},{"key":"7055_CR13","doi-asserted-by":"publisher","first-page":"1207","DOI":"10.1007\/s10044-021-00984-y","volume":"24","author":"A Narin","year":"2021","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 Appl 24:1207\u20131220. https:\/\/doi.org\/10.1007\/s10044-021-00984-y","journal-title":"Pattern Anal Appl"},{"key":"7055_CR14","doi-asserted-by":"publisher","DOI":"10.1101\/2020.05.01.20088211","author":"S Asif","year":"2020","unstructured":"Asif S, Wenhui Y, Jin H et al (2020) Classification of COVID-19 from chest X-ray images using deep convolutional neural networks. medRxiv. https:\/\/doi.org\/10.1101\/2020.05.01.20088211","journal-title":"medRxiv"},{"key":"7055_CR15","doi-asserted-by":"publisher","first-page":"101794","DOI":"10.1016\/j.media.2020.101794","volume":"65","author":"S Minaee","year":"2020","unstructured":"Minaee S, Kafieh R, Sonka M et al (2020) Deep-COVID: predicting COVID-19 from chest X-ray images using deep transfer learning. Med Image Anal 65:101794. https:\/\/doi.org\/10.1016\/j.media.2020.101794","journal-title":"Med Image Anal"},{"key":"7055_CR16","doi-asserted-by":"publisher","first-page":"101734","DOI":"10.1016\/j.bspc.2019.101734","volume":"56","author":"E Ba\u015faran","year":"2020","unstructured":"Ba\u015faran E, C\u00f6mert Z, \u00c7elik Y (2020) Convolutional neural network approach for automatic tympanic membrane detection and classification. Biomed Signal Process Control 56:101734. https:\/\/doi.org\/10.1016\/j.bspc.2019.101734","journal-title":"Biomed Signal Process Control"},{"key":"7055_CR17","doi-asserted-by":"publisher","first-page":"6539","DOI":"10.1109\/TII.2021.3057683","volume":"17","author":"S Tang","year":"2021","unstructured":"Tang S, Wang C, Nie J et al (2021) EDL-COVID: ensemble deep learning for COVID-19 case detection from chest X-Ray images. IEEE Trans Ind Inf 17:6539\u20136549. https:\/\/doi.org\/10.1109\/TII.2021.3057683","journal-title":"IEEE Trans Ind Inf"},{"key":"7055_CR18","doi-asserted-by":"publisher","first-page":"6096","DOI":"10.1007\/s00330-021-07715-1","volume":"31","author":"S Wang","year":"2021","unstructured":"Wang S, Kang B, Ma J et al (2021) A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). Eur Radiol 31:6096\u20136104. https:\/\/doi.org\/10.1007\/s00330-021-07715-1","journal-title":"Eur Radiol"},{"key":"7055_CR19","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 et al (2021) Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble. Expert Syst Appl 165:113909. https:\/\/doi.org\/10.1016\/j.eswa.2020.113909","journal-title":"Expert Syst Appl"},{"key":"7055_CR20","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1109\/RBME.2020.2987975","volume":"14","author":"F Shi","year":"2021","unstructured":"Shi F, Wang J, Shi J et al (2021) Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng 14:4\u201315. https:\/\/doi.org\/10.1109\/RBME.2020.2987975","journal-title":"IEEE Rev Biomed Eng"},{"key":"7055_CR21","doi-asserted-by":"crossref","unstructured":"Lin H, Upchurch P, Bala K (2019) Block annotation: better image annotation with sub-image decomposition. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision. pp 5290\u20135300","DOI":"10.1109\/ICCV.2019.00539"},{"key":"7055_CR22","doi-asserted-by":"publisher","first-page":"7341","DOI":"10.1109\/JSEN.2015.2475626","volume":"15","author":"H Ghayvat","year":"2015","unstructured":"Ghayvat H, Liu J, Mukhopadhyay SC, Gui X (2015) Wellness sensor networks: a proposal and implementation for smart home for assisted living. IEEE Sens J 15:7341\u20137348. https:\/\/doi.org\/10.1109\/JSEN.2015.2475626","journal-title":"IEEE Sens J"},{"key":"7055_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06139-8","author":"H Ghayvat","year":"2021","unstructured":"Ghayvat H, Gope P (2021) Smart aging monitoring and early dementia recognition (SAMEDR): uncovering the hidden wellness parameter for preventive well-being monitoring to categorize cognitive impairment and dementia in community-dwelling elderly subjects through AI. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-021-06139-8","journal-title":"Neural Comput Appl"},{"key":"7055_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2985395","author":"Y-C Chen","year":"2020","unstructured":"Chen Y-C, Lin Y-Y, Yang M-H, Huang J-B (2020) Show, match and segment: joint weakly supervised learning of semantic matching and object co-segmentation. IEEE Trans Pattern Anal Mach Intell. https:\/\/doi.org\/10.1109\/TPAMI.2020.2985395","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7055_CR25","doi-asserted-by":"publisher","first-page":"548","DOI":"10.3390\/info11120548","volume":"11","author":"M Maia","year":"2020","unstructured":"Maia M, Pimentel JS, Pereira IS et al (2020) Convolutional support vector models: prediction of coronavirus disease using chest X-rays. Information 11:548. https:\/\/doi.org\/10.3390\/info11120548","journal-title":"Information"},{"key":"7055_CR26","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.procs.2016.07.027","volume":"90","author":"H Alahmer","year":"2016","unstructured":"Alahmer H, Ahmed A (2016) Computer-aided classification of liver lesions from CT images based on multiple ROI. Procedia Comput Sci 90:80\u201386. https:\/\/doi.org\/10.1016\/j.procs.2016.07.027","journal-title":"Procedia Comput Sci"},{"key":"7055_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05683-z","author":"VDA Kumar","year":"2021","unstructured":"Kumar VDA, Sharmila S, Kumar A et al (2021) A novel solution for finding postpartum haemorrhage using fuzzy neural techniques. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05683-z","journal-title":"Neural Comput Appl"},{"key":"7055_CR28","unstructured":"Zhao J, Zhang Y, He X, Xie P (2020) COVID-CT-dataset: a CT scan dataset about COVID-19. arXiv Prepr arXiv arXiv:2003.13865"},{"key":"7055_CR29","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2020.00357","author":"C Iwendi","year":"2020","unstructured":"Iwendi C, Bashir AK, Peshkar A et al (2020) COVID-19 patient health prediction using boosted random forest algorithm. Front Public Heal. https:\/\/doi.org\/10.3389\/fpubh.2020.00357","journal-title":"Front Public Heal"},{"key":"7055_CR30","doi-asserted-by":"publisher","first-page":"132665","DOI":"10.1109\/ACCESS.2020.3010287","volume":"8","author":"MEH Chowdhury","year":"2020","unstructured":"Chowdhury MEH, Rahman T, Khandakar A et al (2020) Can AI Help in screening viral and COVID-19 pneumonia? IEEE Access 8:132665\u2013132676. https:\/\/doi.org\/10.1109\/ACCESS.2020.3010287","journal-title":"IEEE Access"},{"key":"7055_CR31","doi-asserted-by":"crossref","unstructured":"Mehre SA, Mukhopadhyay S, Dutta A, Harsha NC, Dhara AK, Khandelwal N (2016) An automated lung nodule detection system for CT images using synthetic minority oversampling. In Medical Imaging 2016: Computer-Aided Diagnosis, Vol 9785. International Society for Optics and Photonics, p 97850H","DOI":"10.1117\/12.2216357"},{"key":"7055_CR32","doi-asserted-by":"publisher","first-page":"5780","DOI":"10.3390\/s20205780","volume":"20","author":"M Awais","year":"2020","unstructured":"Awais M, Ghayvat H, Krishnan Pandarathodiyil A et al (2020) Healthcare professional in the loop (HPIL): classification of standard and oral cancer-causing anomalous regions of oral cavity using textural analysis technique in autofluorescence imaging. Sensors 20:5780. https:\/\/doi.org\/10.3390\/s20205780","journal-title":"Sensors"},{"key":"7055_CR33","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1109\/TMI.2017.2750210","volume":"37","author":"S Manivannan","year":"2018","unstructured":"Manivannan S, Li W, Zhang J et al (2018) Structure prediction for gland segmentation with hand-crafted and deep convolutional features. IEEE Trans Med Imaging 37:210\u2013221. https:\/\/doi.org\/10.1109\/TMI.2017.2750210","journal-title":"IEEE Trans Med Imaging"},{"key":"7055_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.scs.2021.102798","volume":"69","author":"H Ghayvat","year":"2021","unstructured":"Ghayvat H, Awais M, Gope P et al (2021) ReCognizing suspect and predicting the spread of contagion based on mobile phone location data (COUNTERACT): a system of identifying COVID-19 infectious and hazardous sites, detecting disease outbreaks based on the internet of things, edge computing, and. Sustain Cities Soc 69:102798. https:\/\/doi.org\/10.1016\/j.scs.2021.102798","journal-title":"Sustain Cities Soc"},{"key":"7055_CR35","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1109\/MNET.011.2000741","volume":"35","author":"P Zhang","year":"2021","unstructured":"Zhang P, Wang C, Kumar N et al (2021) Artificial intelligence technologies for COVID-19-like epidemics: methods and challenges. IEEE Netw 35:27\u201333. https:\/\/doi.org\/10.1109\/MNET.011.2000741","journal-title":"IEEE Netw"},{"key":"7055_CR36","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1016\/j.future.2021.05.019","volume":"124","author":"A Barnawi","year":"2021","unstructured":"Barnawi A, Chhikara P, Tekchandani R et al (2021) Artificial intelligence-enabled Internet of Things-based system for COVID-19 screening using aerial thermal imaging. Future Gener Comput Syst 124:119\u2013132. https:\/\/doi.org\/10.1016\/j.future.2021.05.019","journal-title":"Future Gener Comput Syst"},{"key":"7055_CR37","doi-asserted-by":"publisher","first-page":"1561","DOI":"10.1109\/JBHI.2017.2771211","volume":"22","author":"NB Marvasti","year":"2018","unstructured":"Marvasti NB, Yoruk E, Acar B (2018) Computer-aided medical image annotation: preliminary results with liver lesions in CT. IEEE J Biomed Heal Inf 22:1561\u20131570. https:\/\/doi.org\/10.1109\/JBHI.2017.2771211","journal-title":"IEEE J Biomed Heal Inf"},{"key":"7055_CR38","doi-asserted-by":"publisher","first-page":"90495","DOI":"10.1109\/ACCESS.2020.2993803","volume":"8","author":"K Patel","year":"2020","unstructured":"Patel K, Mehta D, Mistry C et al (2020) Facial sentiment analysis using AI techniques: state-of-the-art, taxonomies, and challenges. IEEE Access 8:90495\u201390519. https:\/\/doi.org\/10.1109\/ACCESS.2020.2993803","journal-title":"IEEE Access"},{"key":"7055_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2020.103792","volume":"121","author":"T Ozturk","year":"2020","unstructured":"Ozturk T, Talo M, Yildirim EA et al (2020) Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput Biol Med 121:103792. https:\/\/doi.org\/10.1016\/j.compbiomed.2020.103792","journal-title":"Comput Biol Med"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07055-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07055-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07055-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T09:56:27Z","timestamp":1685699787000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07055-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,1]]},"references-count":39,"journal-issue":{"issue":"20","published-print":{"date-parts":[[2023,7]]}},"alternative-id":["7055"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07055-1","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,1]]},"assertion":[{"value":"26 February 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 January 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2022","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 have no conflict to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}