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CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
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In this retrospective case series, chest CT scans of 21 symptomatic patients from China infected with the 2019 novel coronavirus (2019-nCoV) were reviewed, with emphasis on identifying and characterizing the most common findings. Typical CT findings included bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities, sometimes with a rounded morphology and a peripheral lung distribution. Notably, lung cavitation, discrete pulmonary nodules, pleural effusions, and lymphadenopathy were absent. Follow-up imaging in a subset of patients during the study time window often demonstrated mild or moderate progression of disease, as manifested by increasing extent and density of lung opacities.
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Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Fleischner Society: Glossary of Terms for Thoracic Imaging
Hansell, D. M., Bankier, A. A., MacMahon, H., McLoud, T. C., Müller, N. L., & Rémy, J. (2008) · Radiology · doi:10.1148/radiol.2462070712 · Cited by 4,189
Radiographic and CT Features of Viral Pneumonia
Koo, H. J., Lim, S., Choe, J., Choi, S., Sung, H., & Do, K. (2018) · Radiographics · doi:10.1148/rg.2018170048 · Cited by 718
Severe Acute Respiratory Syndrome: Temporal Lung Changes at Thin-Section CT in 30 Patients
OOI, G. C., Khong, P. L., Müller, N. L., Yiu, W. C., Zhou, L. J., Ho, J., LAM, B. K., Nicolaou, S., & TSANG, K. W. T. (2004) · Radiology · doi:10.1148/radiol.2303030853 · Cited by 329
Wong, K. T., Antonio, G. E., Hui, D. S. C., Lee, N. L. S., Yuen, E. H. Y., Wu, A. K. L., Leung, C. B., Rainer, T. H., Cameron, P. A., Chung, S. S. C., et al. (2003) · Radiology · doi:10.1148/radiol.2282030593 · Cited by 289
Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Infection: Chest CT Findings
Ajlan, A. M., Ahyad, R. A., Jamjoom, L. G., Alharthy, A., & Madani, T. A. (2014) · American Journal of Roentgenology · doi:10.2214/ajr.14.13021 · Cited by 286
Paul, N. S., Roberts, H. C., BUTANY, J. W., Chung, T., Gold, W. L., Mehta, S., Konen, E., Rao, A., Provost, Y. L., Hong, H., et al. (2004) · Radiographics · doi:10.1148/rg.242035193 · Cited by 98
Choi, W. J., Lee, K. N., Kang, E., & Lee, H. (2016) · Korean Journal of Radiology · doi:10.3348/kjr.2016.17.1.166 · Cited by 45
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Could Spectral CT Have a Potential Benefit in Coronavirus Disease (COVID-19)?
Daoud, B., Cazejust, J., Tavolaro, S., Durand, S., Pommier, R., Hamrouni, A., & Bornet, G. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.23546 · Cited by 29
Zhang, T., Huang, W., Guan, W., Hong, Z., Gao, J., Gao, G., Wu, G., & Qin, Y.-Y. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-2021-06 · Cited by 29
Ning, M. S., McAleer, M. F., Jeter, M. D., Minsky, B. D., Ghafar, R. A., Robinson, I. J., Nitsch, P. L., Zaebst, D. J., Todd, S. E., Nguyen, J. L., et al. (2020) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2020.05.037 · Cited by 28
Calderon-Ramirez, S., Giri, R., Yang, S., Moemeni, A., Umana, M., Elizondo, D. A., Torrents‐Barrena, J., & Molina‐Cabello, M. A. (2021) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr48806.2021.9412946 · Cited by 29
Insights of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) pandemic: a current review
Choudhary, J., Dheeman, S., Sharma, V. K., Katiyar, P., Karn, S. K., Sarangi, M. K., Chauhan, A. K., Verma, G., & Baliyan, N. (2021) · Biological Procedures Online · doi:10.1186/s12575-020-00141-5 · Cited by 28
Zhou, X., Pu, Y., Zhang, D., Xia, Y., Guan, Y., Liu, S., & Fan, L. (2021) · Acta Radiologica · doi:10.1177/0284185121992655 · Cited by 28
Huang, S., Zhao, S., Luo, H., Wu, Z., Wu, J., Xia, H., & Chen, X. (2021) · BMC Pulmonary Medicine · doi:10.1186/s12890-021-01479-6 · Cited by 28
Greffier, J., Hoballah, A., Sadate, A., de Oliveira, F., Claret, P., de Forges, H., Loubet, P., Mauboussin, J., Hamard, A., Bérégi, J.-P., et al. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-20-1176 · Cited by 28
Li, Q., Ning, J., Yuan, J., & Xiao, L. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104837 · Cited by 28
Recent advances in methods for the diagnosis of Corona Virus Disease 2019
Guo, J., Ge, J., & Guo, Y. (2021) · Journal of Clinical Laboratory Analysis · doi:10.1002/jcla.24178 · Cited by 28
Optimizing lung cancer radiation treatment worldwide in COVID-19 outbreak
Liao, Z. X., del Campo, E. R., Salem, A., Pang, Q., Liu, H., & Guerra, J. L. L. (2020) · Lung Cancer · doi:10.1016/j.lungcan.2020.05.029 · Cited by 28
Verma, A. K., Inturi, V., Saurabh, P., Radhika, S., Sabareesh, G. R., & Rajkumar, S. (2021) · Expert Systems with Applications · doi:10.1016/j.eswa.2021.115650 · Cited by 30
Fu, H., Xu, H., Zhang, N., Xu, H., Li, Z., Chen, H., Xu, R., Sun, R., Wen, L., Xie, L., et al. (2020) · medRxiv · doi:10.1101/2020.03.19.20038315 · Cited by 28
Self-citationGASNet: Weakly-supervised Framework for COVID-19 Lesion SegmentationPreprint
Xu, Z., Cao, Y., Jin, C., Shao, G., Liu, X., Zhou, J., Shi, H., & Feng, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.09456 · Cited by 29
Dharawat, A., Lourentzou, I., Morales, A., & Zhai, C. (2020) · arXiv (Cornell University) · Cited by 28
COVID-19: A case series to support radiographer preliminary clinical evaluation
Woznitza, N., Nair, A., & Hare, S. S. (2020) · Radiography · doi:10.1016/j.radi.2020.04.002 · Cited by 27
Xia, T., Li, J., Gao, J., & Xu, X. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2020.0240 · Cited by 27
What should a cardiologist know about coronavirus disease 2019?
Sławiński, G., & Lewicka, E. (2020) · Kardiologia Polska · doi:10.33963/kp.15302 · Cited by 27
Zheng, Z., Yao, Z., Wu, K., & Zheng, J. (2020) · Journal of Clinical Virology · doi:10.1016/j.jcv.2020.104396 · Cited by 27
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Saha, S., Kumar, A., Dash, S., & Singhal, M. (2020) · Journal of Burn Care & Research · doi:10.1093/jbcr/iraa086 · Cited by 27
Imaging findings in coronavirus infections: SARS-CoV, MERS-CoV, and SARS-CoV-2
Franquet, T., Jeong, Y. J., Lam, H. Y. S., Wong, H. Y. F., Chang, Y., Chung, M. J., & Lee, K. S. (2020) · British Journal of Radiology · doi:10.1259/bjr.20200515 · Cited by 27
Self-citationFDG-PET/CT images of COVID-19: a comprehensive review
Minamimoto, R., Hotta, M., Ishikane, M., & Inagaki, T. (2020) · Global Health & Medicine · doi:10.35772/ghm.2020.01056 · Cited by 27
Zhang, Y., Yang, W., Liu, D., Cao, Y., Zheng, Y.-Y., Han, Y., Jin, R., Han, Y. K., Wang, X., Pan, A., et al. (2020) · Translational Lung Cancer Research · doi:10.21037/tlcr-20-892 · Cited by 27
Rawashdeh, M. A., & Saade, C. (2020) · La radiologia medica · doi:10.1007/s11547-020-01271-2 · Cited by 27
Recent advances in the nucleic acid-based diagnostic tool for coronavirus
Prabhakar, P. K., & Lakhanpal, J. (2020) · Molecular Biology Reports · doi:10.1007/s11033-020-05889-3 · Cited by 27
Cai, Q., Du, S., Si, G., Huang, G., Zheng, Z., Li, S., Wang, X., Li, P., Lv, P., Hou, G., et al. (2020) · BMC Medical Imaging · doi:10.1186/s12880-020-00521-z · Cited by 27
Self-citationHuang, Z., Liu, X., Wang, R., Zhang, M., Zeng, X., Liu, J., Yang, Y., Liu, X., Zheng, H., Liang, D., et al. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01965-0 · Cited by 27
Self-citationMa, I. W. Y., Hussain, A., Wagner, M. S., Walker, B., Chee, A. C., Arishenkoff, S., Buchanan, B. M., Liu, R., Mints, G., Wong, T., et al. (2020) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15571 · Cited by 27
Zimmermann, G., Fingerle, A. A., Müller-Leisse, C., Gassert, F. G., von Schacky, C. E., Ibrahim, T., Laugwitz, K., Geisler, F., Spinner, C. D., Haller, B., et al. (2020) · PLoS ONE · doi:10.1371/journal.pone.0244707 · Cited by 27
Lung ultrasonography: A prognostic tool in non-ICU hospitalized patients with COVID-19 pneumonia
Casella, F., Barchiesi, M., Leidi, F., Russo, G., Casazza, G., Valerio, G., Torzillo, D., Ceriani, E., Medico, M. D., Brambilla, A. M., et al. (2021) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2020.12.012 · Cited by 27
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Atzeni, F., Masala, I. F., Rodríguez‐Carrio, J., Garcés, R. R., Gerratana, E., Corte, L. L., Giallanza, M., Nucera, V., Riva, A., Espinosa, G., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10040783 · Cited by 28
Ferreira, J. R., Cárdenas, D. A. C., Moreno, R. A., Rebelo, M. D. S., Krieger, J. E., & Gutierrez, M. A. (2021) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-021-00421-w · Cited by 27
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Merdji, H., Mayeur, S., Schenck, M., Oulehri, W., Clère-Jehl, R., Cunat, S., Herbrecht, J.-É., Janssen-Langenstein, R., Nicolae, A., Helms, J., et al. (2021) · Medicina Intensiva (English Edition) · doi:10.1016/j.medine.2021.02.005 · Cited by 28
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Caracciolo, M., Correale, P., Mangano, C., Foti, G., Falcone, C., Macheda, S., Cuzzola, M., Conte, M., Falzea, A. C., Iuliano, E., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.613070 · Cited by 29
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Zheng, R., Zheng, Y., & Dongye, C. (2021) · Scientific Programming · doi:10.1155/2021/9999368 · Cited by 27
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Yazdi, N. A., Ghadery, A. H., SeyedAlinaghi, S., Jafari, F., Jafari, S., Hasannezad, M., Koochak, H. E., Salehi, M., Manshadi, S. A. D., Meidani, M., et al. (2021) · Virology Journal · doi:10.1186/s12985-021-01699-6 · Cited by 27
Calderon-Ramirez, S., Yang, S., Elizondo, D. A., & Moemeni, A. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108983 · Cited by 27
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Musha, A., Mamun, A. A., Tahabilder, A., Hossen, M. J., Hossen, B., & Ranjbari, S. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i4.pp3655-3664 · Cited by 27
CdcSegNet: Automatic COVID-19 Infection Segmentation From CT Images
Zhang, J., Chen, D., Ma, D., Ying, C., Sun, X., Xu, X., & Cheng, Y. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3267355 · Cited by 27
Functional CT Contrast Nanoagents for the Tumor Microenvironment
Meng, X., Wu, Y., & Bu, W. (2020) · Advanced Healthcare Materials · doi:10.1002/adhm.202000912 · Cited by 27
Noce, A., Albanese, M., Marrone, G., Lauro, M. D., Zaitseva, A. P., Palazzetti, D., Guerriero, C., Paolino, A., Pizzenti, G., Daniele, F. D., et al. (2021) · Pharmaceuticals · doi:10.3390/ph14040336 · Cited by 28
Qian, K., Deng, Y., Tai, Y., Peng, J., Peng, H., & Jiang, L. (2020) · medRxiv · doi:10.1101/2020.02.14.20021535 · Cited by 27
Zhou, Y., Mei, S., Wang, J., Xu, Q., Zhang, Z., Qin, S., Feng, J., Li, C., Xing, S., Wang, W., et al. (2024) · eClinicalMedicine · doi:10.1016/j.eclinm.2024.102772 · Cited by 26
Virot, E., Mathien, C., Pointurier, V., Poidevin, A., Labro, G., Pinto, L., Jandeaux, L.-M., Mootien, J., & Kuteifan, K. (2021) · Critical Care Science · doi:10.5935/0103-507x.20210007 · Cited by 26
Imagerie de la pneumonie COVID-19
Lodé, B., Jalaber, C., Orcel, T., Morcet-Delattre, T., Crespin, N., Voisin, S., Billard, F., Luzi, S., Lapotre, T., Lentz, P.-A., et al. (2020) · Journal d imagerie diagnostique et interventionnelle · doi:10.1016/j.jidi.2020.04.011 · Cited by 26
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Li, J., Long, X., Zhang, Q., Fang, X., Fang, F., Lv, X., Zhang, D., Sun, Y., Li, N., Hu, S., et al. (2020) · Current Neuropharmacology · doi:10.2174/1570159x18666200507085335 · Cited by 26
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Ullah, M. A., Islam, H., Rahman, A., Masud, J., Shweta, D. S., Araf, Y., Sium, S. M. A., & Sarkar, B. (2020) · Electronic Journal of General Medicine · doi:10.29333/ejgm/8258 · Cited by 26
Jiang, Y., Guo, D., Li, C., Chen, T., & Li, R. (2020) · Radiology of Infectious Diseases · doi:10.1016/j.jrid.2020.05.001 · Cited by 26
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Ferreira‐Santos, D., Maranhao, P. A., & Monteiro‐Soares, M. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-041079 · Cited by 26
Zubieta-Calleja, G. R., Zubieta-DeUrioste, N., Venkatesh, T., Das, K. K., & Soliz, J. (2020) · Reviews on Recent Clinical Trials · doi:10.2174/1574887115666200925141108 · Cited by 26
Watchmaker, J. M., Goldman, D. T., Lee, J. Y., Choi, S., Mills, A. C., Toussie, D., Finkelstein, M. S., Sher, A., Jacobi, A., Bernheim, A., et al. (2020) · Academic Emergency Medicine · doi:10.1111/acem.14148 · Cited by 26
Self-citationMachine learning is the key to diagnose COVID-19: a proof-of-concept study
Gangloff, C., Rafi, S., Bouzillé, G., Soulat, L., & Marc Cuggia, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-86735-9 · Cited by 26
Osmonov, B., Ovchinnikov, L. M., Galazis, C., Emilov, B., Karaibragimov, M., Seitov, M., Vesnin, S. G., Losev, A. G., Levshinskii, V., Popov, I., et al. (2021) · Diagnostics · doi:10.3390/diagnostics11020259 · Cited by 26
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Vollenberg, R., Matern, P., Nowacki, T. M., Fuhrmann, V., Padberg, J.-S., Ochs, K., Schütte‐Nütgen, K., Strauß, M., Schmidt, H., & Tepasse, P. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10051046 · Cited by 26
Ertaş, Y. N., Mahmoodi, M., Shahabipour, F., Jahed, V., Di̇ltemi̇z, S. E., Tutar, R., & Ashammakhi, N. A. (2021) · Emergent Materials · doi:10.1007/s42247-021-00165-x · Cited by 26
Bellos, I., Tavernaraki, K., Stefanidis, K., Michalopoulou, O., Lourida, P., Korompoki, E., Thanou, I., Thanos, L., Pefanis, A. V., & Argyraki, A. (2021) · Respiratory Investigation · doi:10.1016/j.resinv.2021.02.008 · Cited by 26
Srinivasan, S., Selvaraj, G. F., Gopalan, V., Padmanabhan, P., Ramesh, K., Govindan, K., Chandran, A., Dhandapani, P., Krishnasamy, K., & Kitambi, S. S. (2022) · Journal of Global Infectious Diseases · doi:10.4103/jgid.jgid_96_21 · Cited by 26
Chen, S., Liu, M., Pan, D., Deng, J., Yuan, Y., Cheng, X., Xie, T., Xie, L., Zhang, W., Gong, H., et al. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3197666 · Cited by 26
Deep learning-based technique for lesions segmentation in CT scan images for COVID-19 prediction
Afif, M., Ayachi, R., Said, Y., & Atri, M. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-14941-w · Cited by 26
Zhou, M.-E., Liu, Y., Qin, H., Shang, T., Xue, Z., Yang, S., Zhang, H., & Yang, J. (2023) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2023.114530 · Cited by 26
Li, J., Wang, X., Huang, X.-L., Chen, F., Zhang, X., Liu, Y., Luo, G., & Xu, X. (2020) · BMC Medical Imaging · doi:10.1186/s12880-020-00456-5 · Cited by 26
A prediction model based on machine learning for diagnosing the early COVID-19 patientsPreprint
Sun, N., Yang, Y., Tang, L., Dai, Y.-N., Gao, H.-N., Hong-ying, P., & Ju, B. (2020) · medRxiv · doi:10.1101/2020.06.03.20120881 · Cited by 26
Self-citationZeng, Q., Li, G., Ji, F., Ma, S., Zhang, G., Xu, J., Lin, W., Xu, G., Zhang, G., Li, G., et al. (2020) · Transboundary and Emerging Diseases · doi:10.1111/tbed.13674 · Cited by 25
High-resolution CT features of COVID-19 pneumonia in confirmed cases
Omar, S. F., Motawea, A. M., & Yasin, R. (2020) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-020-00236-9 · Cited by 25
Liu, N., He, G., Yang, X., Chen, J., Wu, J., Ma, M., Lu, W., Li, Q., Cheng, T., & Huang, X. (2020) · BMC Medical Imaging · doi:10.1186/s12880-020-00491-2 · Cited by 25
Wang, X., Hu, X., Tan, W., Mazzone, P. J., Mireles‐Cabodevila, E., Han, X., Huang, P., Hu, W., Dweik, R. A., & Cheng, Z. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.24044 · Cited by 25
Garg, M. K., Gupta, P. K., Maralakunte, M., Kumar, P. M., Sinha, A., Kang, M., Agarwal, R., & Sandhu, M. S. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.11.021 · Cited by 25
Atypical presentations of coronavirus disease 2019 (COVID-19) from onset to readmission
Yang, Z., Chen, X., Huang, R., Lin, D., Lin, D., Yang, Z., Sun, H., Qiu, J., Qiu, J., Tang, Y., et al. (2021) · BMC Infectious Diseases · doi:10.1186/s12879-020-05751-8 · Cited by 25
Guarnera, A., Podda, P., Santini, E., Paolantonio, P., & Laghi, A. (2021) · Insights into Imaging · doi:10.1186/s13244-021-00967-x · Cited by 25
Zhao, L., Wang, X., Xiong, Y., Fan, Y., Zhou, Y., & Zhu, W. (2021) · Medicine · doi:10.1097/md.0000000000025232 · Cited by 25
Heydari, K., Rismantab, S., Shamshirian, A., Lotfi, P., Shadmehri, N., Houshmand, P., Zahedi, M., Shamshirian, D., Bathaeian, S., & Alizadeh‐Navaei, R. (2022) · Tabari Biomedical Student Research Journal · doi:10.18502/tbsrj.v4i1.8772 · Cited by 25
Sadik, F., Dastider, A. G., Subah, M. R., Mahmud, T., & Fattah, S. A. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105806 · Cited by 25
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Jawahar, M., Anbarasi, L. J., Ravi, V., Prassanna, J., Jasmine, S. G., Manikandan, R., Sekaran, R., & Suthendran, K. (2022) · Health and Technology · doi:10.1007/s12553-022-00688-1 · Cited by 25
Structural Attention Graph Neural Network for Diagnosis and Prediction of COVID-19 Severity
Liu, Y., Li, H., Luo, T., Zhang, C., Xiao, Z., Wei, Y., Gao, Y., Shi, F., Shan, F., & Shen, D. G. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3226575 · Cited by 25
Wang, E., Liu, A., Wang, Z., Shang, X., Zhang, L., Jin, Y., Ma, Y., Zhang, L., Bai, T., Song, J., et al. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2022.978237 · Cited by 26
Ahamed, M. K. U., Islam, M. M., Uddin, M. A., Akhter, A., Acharjee, U. K., Paul, B. K., & Moni, M. A. (2023) · Diagnostics · doi:10.3390/diagnostics13030551 · Cited by 26
Sanhueza, S., Vidal, M., Hernandez, M. A., Henríquez‐Beltrán, M., Cabrera, C., Quiroga, R. A., Antilef, B., Aguilar, K. P., Castillo, D. A., Llerena, F., et al. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1271863 · Cited by 25
Asif, S., Ain, Q. U., Al-Sabri, R., & Abdullah, M. (2024) · Journal of Computational Science · doi:10.1016/j.jocs.2024.102324 · Cited by 25
Laskar, P., Yallapu, M. M., & Chauhan, S. C. (2020) · Diseases · doi:10.3390/diseases8030030 · Cited by 29
Kang, M., Chikontwe, P., Luna, M., Hong, K. S., Jang, J. G., Park, J., Shin, K. C., Ahn, J.-H., & Park, S. H. (2021) · Journal of Korean Medical Science · doi:10.3346/jkms.2021.36.e46 · Cited by 26
Why is chest CT important for early diagnosis of COVID-19? Prevalence mattersPreprint
Esposito, A., Palmisano, A., Scotti, G. M., Morelli, M. J., Vignale, D., COBELLI, F. D., Tonon, G., & Tacchetti, C. (2020) · medRxiv · doi:10.1101/2020.03.30.20047985 · Cited by 25
Chen, M., Tu, C., Tan, C., Zheng, X., Wang, X. H., Wu, J., Huang, Y., Wang, Z., Yan, Y., Li, Z., et al. (2020) · medRxiv · doi:10.1101/2020.04.06.20054890 · Cited by 25
Self-citationCT morphology of COVID-19: Case report and review of literature
Hamer, O. W., Salzberger, B., Gebauer, J., Stroszczynski, C., & Pfeifer, M. A. (2020) · RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren · doi:10.1055/a-1142-4094 · Cited by 24
Andréjak, C., Blanc, F. –., Costes, F., Crestani, B., Debieuvre, D., Perez, T., Philippe, B., Plantier, L., Schlemmer, F., Sésé, L., et al. (2020) · Revue des Maladies Respiratoires · doi:10.1016/j.rmr.2020.05.001 · Cited by 24
Pulmonary ct manifestations of COVID-19: changes within 2 weeks duration from presentation
Sultan, O. M., Al-Tameemi, H. N., Alghazali, D. M., Abed, M., Ghniem, M. N. A., Hawiji, D. A., Alwateefee, E. A., Shubbar, H. D. A., Tauah, A. H., Ibraheem, N. M., et al. (2020) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-020-00223-0 · Cited by 24
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Chen, Z.-H., Li, Y.-J., Wang, X.-J., Ye, Y.-F., Wu, B.-L., Zhang, Y., Xuan, W.-L., Bao, J.-F., & Deng, X. (2020) · Medicine · doi:10.1097/md.0000000000020837 · Cited by 24
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Gong, X., Song, L., Li, H., Li, L., Jin, W., Yu, K., Zhang, X., Li, H., Ke, H., & Lu, Z. (2020) · PLoS ONE · doi:10.1371/journal.pone.0235134 · Cited by 24
Bloukh, S. H., Edis, Z., Shaikh, A. A., & Pathan, H. M. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17155616 · Cited by 25
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Xiang, C., Ji, L., Zhou, J., Guan, L., Yang, C., & Chai, C. (2020) · BioMed Research International · doi:10.1155/2020/5436025 · Cited by 24
Zhan, J., Li, H., Yu, H., Liu, X., Zeng, X., Peng, D., & Zhang, W. (2020) · European Radiology · doi:10.1007/s00330-020-07201-0 · Cited by 24
Self-citationDurhan, G., Düzgün, S. A., Demirkazık, F., Irmak, İ., İdilman, İ. S., Akpınar, M. G., Akpınar, E., Öcal, S., Dizman, G. T., Topeli̇, A., et al. (2020) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2020.20407 · Cited by 24
Risk Factors for Myocardial Injury in Patients with Coronavirus Disease 2019 in China
Fan, Q., Zhu, H., Zhao, J., Zhuang, L., Zhang, H., Xie, H., Zhang, R., Granada, J. F., Xiang, X., Hu, W., et al. (2020) · ESC Heart Failure · doi:10.1002/ehf2.13022 · Cited by 24
Zotzmann, V., Lang, C. N., Wengenmayer, T., Bemtgen, X., Schmid, B., Müller‐Peltzer, K., Supady, A., Bode, C., Duerschmied, D., & Staudacher, D. L. (2020) · Journal of Thrombosis and Thrombolysis · doi:10.1007/s11239-020-02323-0 · Cited by 24
Lung Ultrasound in the Emergency Department for Early Identification of COVID-19 Pneumonia
Zanforlin, A., Strapazzon, G., Falk, M., Gallina, V., Viteritti, A., Valzolgher, L., Guardia, M. L., Ferro, F., Pagani, L., & Vezzali, N. (2020) · Respiration · doi:10.1159/000512782 · Cited by 24
Karimian, M., & Azami, M. (2020) · Polish Journal of Radiology · doi:10.5114/pjr.2021.103379 · Cited by 24
Fouad, S. H., Allam, M. F., Taha, S., Okba, A. A., Hosny, A., Moneer, M., & Roman, S. W. (2021) · Journal of International Medical Research · doi:10.1177/03000605211030124 · Cited by 25
Terra, P. O. C., Donadel, C. D., de Oliveira, L. C. O., Menegüeti, M. G., Auxiliadora‐Martins, M., Calado, R. T., & Santis, G. C. D. (2022) · Health Science Reports · doi:10.1002/hsr2.514 · Cited by 24
A semi-supervised learning approach for COVID-19 detection from chest CT scans
Zhang, Y., Su, L., Liu, Z., Tan, W., Jiang, Y., & Cheng, C. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.06.076 · Cited by 24
A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices
Alyafei, K. A., Ahmed, R., Abir, F. F., Chowdhury, M. E. H., & Naji, K. K. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106070 · Cited by 24
Bondugula, R. K., Udgata, S. K., & Bommi, N. S. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-021-05879-y · Cited by 25
Metadata
- Authors
- Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., Jacobi, A., Li, K., Li, S., & Shan, H.
- Affiliations
- Affiliated Hospital of Qingdao University (CN)
- Sun Yat-sen University (CN)
- Corresponding author
- Shan, Hong
- ORCID
- Venue
- Radiology
- Published
- 2020-04
- Volume
- 295
- Issue
- 1
- Pages
- 202-207
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC7194022
- OpenAlex
- W3004906315
- Cited by
- 2,751
- References
- 8
- Citations by year
- 2026: 15 · 2025: 48 · 2024: 117 · 2023: 217 · 2022: 412 · 2021: 776 · 2020: 1162
- FWCI
- 83.76
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Infectious Diseases › COVID-19 Clinical Research Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Keywords
- SARS-CoV-2, COVID-19 pandemic, chest CT findings, ground-glass opacities, lung consolidation, lung opacity, disease progression, pulmonary nodules, pleural effusion, lymphadenopathy, lung cavitation
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, COVID-19 Testing, Adult, Aged, Female, Humans, Lung, Male, Middle Aged, Pneumonia, Viral, Retrospective Studies, Tomography, X-Ray Computed, Coronavirus Infections, Disease Progression, Clinical Laboratory Techniques, Severe Acute Respiratory Syndrome
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