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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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From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [84]
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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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Salaffi, F., Carotti, M., Tardella, M., Borgheresi, A., Agostini, A., Minorati, D., Marotto, D., Carlo, M. D., Galli, M., Giovagnoni, A., et al. (2020) · Medicine · doi:10.1097/md.0000000000022433 · Cited by 46
García‐Ortega, A., Oscullo, G., Calvillo, P., López-Reyes, R., Méndez, R., Gómez-Olivas, J. D., Bekki, A., Fonfría, C., Trilles-Olaso, L., Zaldívar, E., et al. (2021) · Journal of Infection · doi:10.1016/j.jinf.2021.01.003 · Cited by 46
Munker, D., Veit, T., Barton, J., Mertsch, P., Mümmler, C., Osterman, A., Khatamzas, E., Barnikel, M., Hellmuth, J. C., Muenchhoff, M., et al. (2021) · Infection · doi:10.1007/s15010-021-01669-8 · Cited by 52
Review on the COVID-19 pandemic prevention and control system based on AI
Yi, J., Zhang, H., Mao, J., Chen, Y., Zhong, H., & Wang, Y. (2022) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2022.105184 · Cited by 50
Malik, H., Anees, T., Chaudhry, M. U., Goňo, R., Jasiński, M., Leonowicz, Z. M., & Bernat, P. (2023) · IEEE Access · doi:10.1109/access.2023.3267492 · Cited by 50
Khan, R., Taj, S., Ma, X., Noor, A., Zhu, H., Khan, J. A., Khan, Z. U., & Khan, S. U. (2024) · Scientific Reports · doi:10.1038/s41598-024-77196-x · Cited by 47
Jamshidi, M. B., Lalbakhsh, A., Talla, J., Peroutka, Z., Roshani, S., Matousek, V., Roshani, S., Mirmozafari, M., Malek, Z., Spada, L. L., et al. (2021) · Studies in systems, decision and control · doi:10.1007/978-3-030-67716-9_2 · Cited by 46
The Current Clinically Relevant Findings on COVID-19 Pandemic
Pergolizzi, J. V., Magnusson, P., LeQuang, J. A., Breve, F., Paladini, A., Rekatsina, M., Yeam, C. T., Imani, F., Saltelli, G., Taylor, R., et al. (2020) · Anesthesiology and Pain Medicine · doi:10.5812/aapm.103819 · Cited by 45
Zhu, Z., Xingming, Z., Tao, G., Dan, T., Li, J., Chen, X., Yang, L., Zhou, Z., Zhang, X., Zhou, J., et al. (2021) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-020-00408-1 · Cited by 45
Fu, L., Li, Y., Cheng, A., Pang, P., & Shu, Z. (2020) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000544 · Cited by 45
Nejat, R., & Sadr, A. S. (2020) · In Silico Pharmacology · doi:10.1007/s40203-020-00058-7 · Cited by 45
Brogna, B., Brogna, C., Petrillo, M., Conte, A. M., Benincasa, G., Montano, L., & Piscopo, M. (2021) · Medicina · doi:10.3390/medicina57030290 · Cited by 46
The Many Faces of COVID-19: Spectrum of Imaging Manifestations
Kay, F. U., & Abbara, S. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200037 · Cited by 45
Ho, T.-T., Tran, K.-D., & Huang, Y. (2022) · Sensors · doi:10.3390/s22103728 · Cited by 44
Liu, J., Chen, T., Yang, H., Cai, Y., Yu, Q., Chen, J., Chen, Z., Shang, Q., Ma, C., Chen, X., et al. (2020) · European Radiology · doi:10.1007/s00330-020-06916-4 · Cited by 44
Clinical characteristics of COVID-19 and its comparison with influenza pneumonia
Qu, J.-J., Chang, L. K., Tang, X., Du, Y., Yang, X., Liu, X., Han, P., & Xue, Y. (2020) · Acta Clinica Belgica · doi:10.1080/17843286.2020.1798668 · Cited by 44
Eslami, V., Abrishami, A., Zarei, E., Khalili, N., Baharvand, Z., & Sanei‐Taheri, M. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.09.012 · Cited by 44
Deep neural networks ensemble to detect COVID-19 from CT scans
Aversano, L., Bernardi, M. L., Cimitile, M., & Pecori, R. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108135 · Cited by 44
A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images
Attallah, O. (2022) · Digital Health · doi:10.1177/20552076221092543 · Cited by 44
An Efficient Deep Learning Method for Detection of COVID-19 Infection Using Chest X-ray Images
Nayak, S. R., Nayak, D. R., Sinha, U., Arora, V., & Pachori, R. B. (2022) · Diagnostics · doi:10.3390/diagnostics13010131 · Cited by 49
Correlation of Pectoralis Muscle Volume and Density with Severity of COVID-19 Pneumonia in Adults
Hocaoğlu, E., Örs, S., Yıldız, Ö., & İnci, E. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.11.017 · Cited by 44
An explainable AI system for automated COVID-19 assessment and lesion categorization from CT-scans
Pennisi, M., Kavasidis, I., Spampinato, C., Schininà, V., Palazzo, S., Salanitri, F. P., Bellitto, G., Rundo, F., Aldinucci, M., Cristofaro, M., et al. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102114 · Cited by 46
Clinical Characteristics of Coronavirus Disease 2019 (COVID-19): An Updated Systematic ReviewPreprint
Fang, Z., Fang, Y., Wu, K., Lai, K., Sun, X., 钟南山, & Liu, Z. (2020) · medRxiv · doi:10.1101/2020.03.07.20032573 · Cited by 44
Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 ClassPreprint
Kadry, S., Rajinikanth, V., Rho, S., Raja, N. S. M., Rao, V. S., & Thanaraj, K. P. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.13122 · Cited by 44
Parry, A. H., Wani, A. H., Yaseen, M., Dar, K. A., Choh, N. A., Khan, N. A., Shah, N. N., & Jehangir, M. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109147 · Cited by 43
CT features of COVID-19 patients with two consecutive negative RT-PCR tests after treatment
Fu, Z., Tang, N., Chen, Y., Ma, L., Wei, Y., Lu, Y., Ye, K., Liu, H., Tang, F., Huang, G., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-68509-x · Cited by 43
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Smargiassi, A., Soldati, G., Torri, E., Mento, F., Milardi, D., Giacomo, P. D., Matteis, G. D., Burzo, M. L., Larici, A. R., Pompili, M., et al. (2020) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15428 · Cited by 43
Medical imaging and computational image analysis in COVID-19 diagnosis: A review
Nabavi, S., Ejmalian, A., Moghaddam, M. E., Abin, A. A., Frangi, A. F., Mohammadi, M. H., & Rad, H. S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104605 · Cited by 44
Agostini, A., Borgheresi, A., Carotti, M., Ottaviani, L., Badaloni, M., Floridi, C., & Giovagnoni, A. (2020) · La radiologia medica · doi:10.1007/s11547-020-01298-5 · Cited by 43
CT chest findings in patients infected with COVID-19: review of literature
Hefeda, M. M. (2020) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-020-00355-3 · Cited by 43
Clinical characteristics of COVID-19 complicated with pleural effusion
Zhan, N., Guo, Y., Tian, S., Huang, B., Tian, X., Zou, J., Xiong, Q., Tang, D., Zhang, L., & Dong, W. (2021) · BMC Infectious Diseases · doi:10.1186/s12879-021-05856-8 · Cited by 43
Attallah, O. (2023) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2022.104750 · Cited by 43
Pulmonary fibrosis: A short- or long-term sequelae of severe COVID-19?
Zheng, Z., Peng, F., & Zhou, Y. (2023) · Chinese Medical Journal - Pulmonary and Critical Care Medicine · doi:10.1016/j.pccm.2022.12.002 · Cited by 45
Fields, B. K. K., Demirjian, N. L., & Gholamrezanezhad, A. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.08.014 · Cited by 43
Xie, J., Wang, Q., Xu, Y., Zhang, T., Chen, L., Zuo, X., Liu, J., Huang, L., Zhan, P., Lv, T., et al. (2021) · Annals of Palliative Medicine · doi:10.21037/apm-20-1863 · Cited by 43
Deep learning for COVID-19 chest CT (computed tomography) image analysis: A lesson from lung cancer
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Machine learning for medical imaging‐based COVID‐19 detection and diagnosis
Rehouma, R., Büchert, M., & Chen, Y. P. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22504 · Cited by 44
Zhang, Y., Liao, Q., Yuan, L., Zhu, H., Xing, J., & Zhang, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3106341 · Cited by 43
Franquet, T., Giménez, A., Ketai, L. H., Mazzini, S., Rial, A., Pomar, V., & Domingo, P. (2022) · European Radiology · doi:10.1007/s00330-022-08580-2 · Cited by 45
Children and Adolescents Dietary Habits and Lifestyle Changes during COVID-19 Lockdown in Italy
Farello, G., D’Andrea, M., Quarta, A., Grossi, A., Pompili, D., Altobelli, E., Stagi, S., & Balsano, C. (2022) · Nutrients · doi:10.3390/nu14102135 · Cited by 48
Xu, R., Du, M., Li, L., Zhen, Z., Wang, H., & Hu, X. (2020) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims.2020.02.13 · Cited by 42
Miao, C., Jin, M., Li, M., Yang, X., Huang, P., Xiong, H., Huang, P., Zhao, Q., Du, J., & Jiang, H. (2020) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2020.04.051 · Cited by 42
Baratella, E., Crivelli, P., Marrocchio, C., Bozzato, A. M., VITO, A. D., Madeddu, G., Saderi, L., Confalonieri, M., Tenaglia, L., & Cova, M. A. (2020) · Jornal Brasileiro de Pneumologia · doi:10.36416/1806-3756/e20200226 · Cited by 42
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Budimirović, N., Prabhu, E., Antonijević, M., Zivkovic, M. Z., Bačanin, N., Strumberger, I., & Venkatachalam, K. (2022) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.023418 · Cited by 42
Chest computed tomography findings of COVID-19 pneumonia: pictorial essay with literature review
Cellina, M. I., Orsi, M., Pittino, C. V., Toluian, T., & Oliva, G. (2020) · Japanese Journal of Radiology · doi:10.1007/s11604-020-01010-7 · Cited by 42
Hanna, R., Dalvi, S., Sălăgean, T., Pop, I., Bordea, I. R., & Benedicenti, S. (2021) · Journal of Inflammation Research · doi:10.2147/jir.s282213 · Cited by 42
Maior, C. B. S., Santana, J. M., Lins, I. D., & Moura, M. J. C. (2021) · PLoS ONE · doi:10.1371/journal.pone.0247839 · Cited by 45
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Xu, Q., Zhan, X., Zhou, Z., Li, Y., Xie, P., Zhang, S., Li, X., Yu, Y., Zhou, C., Zhang, L. J., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00446-z · Cited by 42
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Tahtabaşı, M., Kılıçaslan, N., Akın, Y., Karaman, E., Gezer, M. C., İçen, Y. K., & Sahiner, F. (2021) · Journal of Clinical Densitometry · doi:10.1016/j.jocd.2021.07.007 · Cited by 42
Shaheed, K., Szczuko, P., Abbas, Q., Hussain, A., & Albathan, M. (2023) · Healthcare · doi:10.3390/healthcare11060837 · Cited by 42
Piras, A., Rizzo, D., Uzzau, S., RIU, G. D., Rubino, S., & Bussu, F. (2020) · Otolaryngology · doi:10.1177/0194599820931793 · Cited by 41
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Zhou, M., Yang, D., Chen, Y., Xu, Y., Xu, J., Jie, Z., Yao, W., Jin, X., Pan, Z., Tan, J., et al. (2021) · Annals of Translational Medicine · doi:10.21037/atm-20-5328 · Cited by 41
Wang, S., Dong, D., Li, L., Li, H., Bai, Y., Hu, Y., Huang, Y., Yu, X., Liu, S., Qiu, X., et al. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3076086 · Cited by 41
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Chest CT in patients with a moderate or high pretest probability of COVID-19 and negative swab
Giannitto, C., Sposta, F. M., Repici, A., Vatteroni, G., Casiraghi, E., Casari, E., Ferraroli, G. M., Fugazza, A., Sandri, M. T., Chiti, A., et al. (2020) · La radiologia medica · doi:10.1007/s11547-020-01269-w · Cited by 40
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Balaha, H. M., Balaha, M. H., & Ali, H. A. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102156 · Cited by 40
A collaborative online AI engine for CT-based COVID-19 diagnosisPreprint
Xu, Y., Ma, L., Yang, F., Chen, Y., Ma, K., Yang, J., Yang, X. Z., Chen, Y., Shu, C., Fan, Z., et al. (2020) · medRxiv · doi:10.1101/2020.05.10.20096073 · Cited by 40
Lei, P., Huang, Z., Liu, G., Wang, P., Song, W., Mao, J., Shen, G., Zhou, S., Qian, W., & Jiao, J. (2020) · Journal of X-Ray Science and Technology · doi:10.3233/xst-200670 · Cited by 39
Lung ultrasound in the diagnosis of COVID-19 infection - A case series and review of the literature
Buda, N., Segura‐Grau, E., Cylwik, J., & Wełnicki, M. T. (2020) · Advances in Medical Sciences · doi:10.1016/j.advms.2020.06.005 · Cited by 39
Cuidados intensivos durante la epidemia de coronavirus 2019
González-Castro, A., Escudero-Acha, P., Peñasco, Y., Leizaola, O., Sánchez, V. M. D. P., & de Lorenzo, A. G. (2020) · Medicina Intensiva · doi:10.1016/j.medin.2020.03.001 · Cited by 47
Jäckel, M., Rilinger, J., Lang, C. N., Zotzmann, V., Kaier, K., Stachon, P., Biever, P. M., Wengenmayer, T., Duerschmied, D., Bode, C., et al. (2020) · Artificial Organs · doi:10.1111/aor.13865 · Cited by 39
FCOD: Fast COVID-19 Detector based on deep learning techniques
Panahi, A. H., Rafiei, A., & Rezaee, A. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100506 · Cited by 40
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de Sousa, P. M., Carneiro, P. C., Oliveira, M. M., Pereira, G. M., da Costa, C. A., de Moura, L. V., Mattjie, C., da Silva, A. M. M., & Patrocínio, A. C. (2021) · Research on Biomedical Engineering · doi:10.1007/s42600-020-00120-5 · Cited by 40
Co-infection of SARS-COV-2 and Influenza A Virus: A Case Series and Fast Review
Xiang, X., Wang, Z.-H., Ye, L., He, X., Wei, X., Ma, Y., Li, H., Chen, L., Wang, X., & Zhou, Q. (2021) · Current Medical Science · doi:10.1007/s11596-021-2317-2 · Cited by 39
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Panetta, K. A., Sanghavi, F. M., Agaian, S. С., & Madan, N. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3069798 · Cited by 40
Ghose, P., Alavi, M., Tabassum, M., Uddin, M. A., Biswas, M., Mahbub, M. K., Gaur, L., Mallik, S., & Zhao, Z. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.980338 · Cited by 41
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Vu, D. M., Ruggiero, M., Choi, W. S., Masri, D. J., Flyer, M. A., Shyknevsky, I., & Stein, E. G. (2020) · Emergency Radiology · doi:10.1007/s10140-020-01775-4 · Cited by 38
Zhang, B., Zhang, J., Chen, H., Chen, L., Chen, Q., Li, M., Chen, Z., You, J., Yang, K., & Zhang, S. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-04854-3 · Cited by 38
Chen, H., Qiu, J., Wu, B., Huang, T., Gao, Y., Wang, Z. P., Chen, Y., & Chen, F. (2020) · European Radiology · doi:10.1007/s00330-020-06978-4 · Cited by 38
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Zhao, L., Yu, K.-L., Zhao, Q., Tian, R., Xie, H., Xie, L., Deng, P., Xie, G., Bao, A., & Du, J. (2020) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2020.07.024 · Cited by 38
Chen, J., Liu, Y., Qin, J., Ruan, C., Zeng, X., Xu, A., Yang, R., Li, J., Cai, H., & Zhang, Z. (2021) · Postgraduate Medical Journal · doi:10.1136/postgradmedj-2021-140674 · Cited by 38
Self-citationLiu, F., Sun, X., Zhang, Y., Lin, G., Wang, J., Liang, X., Li, J.-F., Wang, C.-L., Xing, Z.-T., Chhetri, J. K., et al. (2020) · Critical Care Medicine · doi:10.1097/ccm.0000000000004549 · Cited by 38
Cellina, M. I., Orsi, M., Toluian, T., Pittino, C. V., & Oliva, G. (2020) · Radiography · doi:10.1016/j.radi.2020.04.017 · Cited by 38
Chest CT imaging characteristics of COVID-19 pneumonia in preschool children: a retrospective study
Yang, L., Cao, J., Zhang, X., Liu, G., Wu, X., & Wu, B. (2020) · BMC Pediatrics · doi:10.1186/s12887-020-02140-7 · Cited by 38
Liu, X. F., Zhou, H., Zhou, Y., Wu, X., Zhao, Y., Yang, L., Tan, W., Yuan, M., Ding, X., Zou, J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-66895-w · Cited by 38
Orsi, M., Oliva, G., Toluian, T., Pittino, C. V., Panzeri, M. M., & Cellina, M. I. (2020) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.20-0535 · Cited by 38
COVID-19: A review of the ongoing pandemic
Mahajan, A., Pande, P., Sharma, P., Goyal, D., Kulkarni, T., & Rane, S. U. (2020) · Cancer Research Statistics and Treatment · doi:10.4103/crst.crst_174_20 · Cited by 38
Zhang, H., Du, F., Cao, X., Feng, X.-L., Zhang, H.-P., Wu, Z., Wang, B., Zhang, H., Liu, R., Yang, J.-J., et al. (2021) · BMC Infectious Diseases · doi:10.1186/s12879-021-05897-z · Cited by 38
Gao, J., & Radford, B. J. (2021) · World Medical & Health Policy · doi:10.1002/wmh3.435 · Cited by 41
Ibrahim, M., Youssef, S. M., & Fathalla, K. M. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03282-x · Cited by 38
A stacked ensemble for the detection of COVID-19 with high recall and accuracy
Jangam, E., & Annavarapu, C. S. R. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104608 · Cited by 38
Balashov, V. S., Yan, Y., & Zhu, X. (2021) · Scientific Reports · doi:10.1038/s41598-021-02367-z · Cited by 38
Peng, L., Wang, C., Tian, G., Liu, G., Gan, L., Lu, Y., Yang, J., Chen, M., & Li, Z. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.995323 · Cited by 38
Chronic Pulmonary Manifestations of COVID-19 Infection: Imaging Evaluation
Murphy, M. C., & Little, B. P. (2023) · Radiology · doi:10.1148/radiol.222379 · Cited by 38
Xu, J., Wu, R., Huang, H., Zheng, W., Ren, X., Wu, N., Ji, B., Lv, Y., Liu, Y., & Mi, R. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa207 · Cited by 37
Clinical and CT findings of COVID-19: differences among three age groups
Wang, J., Zhu, X., Xu, Z., Yang, G., Mao, G., Jia, Y., Xie, Z., Wang, J., & Ao, W. (2020) · BMC Infectious Diseases · doi:10.1186/s12879-020-05154-9 · Cited by 37
A comprehensive review of COVID-19 detection with machine learning and deep learning techniques
Das, S., Ayus, I., & Gupta, D. (2023) · Health and Technology · doi:10.1007/s12553-023-00757-z · Cited by 37
Low-Dose Chest CT for the Diagnosis of COVID-19
Schulze-Hagen, M. F., Hübel, C., Meier-Schroers, M., Yüksel, C., Sander, A., Sähn, M.-J., Kleines, M., Isfort, P., Cornelissen, C. G., Lemmen, S., et al. (2020) · Deutsches Ärzteblatt international · doi:10.3238/arztebl.2020.0389 · Cited by 37
Pulmonary artery thrombi are co-located with opacifications in SARS-CoV2 induced ARDS
Müller‐Peltzer, K., Krauß, T., Benndorf, M., Lang, C. N., Bamberg, F., Bode, C., Duerschmied, D., Staudacher, D. L., & Zotzmann, V. (2020) · Respiratory Medicine · doi:10.1016/j.rmed.2020.106135 · Cited by 37
Wu, D., Gong, K., Arru, C. D., Homayounieh, F., Bizzo, B. C., Buch, V., Ren, H., Kim, K., Neumark, N., Xu, P., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.3030224 · Cited by 37
Ullah, H., Ullah, A., Gul, A., Mousavi, T., & Khan, M. W. (2020) · Vacunas (English Edition) · doi:10.1016/j.vacun.2020.09.009 · Cited by 40
Interferon-α2b Treatment for COVID-19 Is Associated with Improvements in Lung Abnormalities
Zhou, Q., MacArthur, M. R., He, X., Wei, X., Zarin, P., Hanna, B. S., Wang, Z.-H., Xiang, X., & Fish, E. N. (2020) · Viruses · doi:10.3390/v13010044 · Cited by 39
Strumilienė, E., Zeleckienė, I., Bliūdžius, R., Samuilis, A., Žvirblis, T., Zablockienė, B., Strumila, A., Gruslys, V., Malinauskienė, L., Kasiulevičius, V., et al. (2021) · Medicina · doi:10.3390/medicina57060568 · Cited by 37
A Novel Multi-Stage Residual Feature Fusion Network for Detection of COVID-19 in Chest X-Ray Images
Fang, Z., Ren, J., MacLellan, C., Li, H., Zhao, H., Hussain, A., & Fortino, G. (2021) · IEEE Transactions on Molecular Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2021.3099367 · Cited by 36
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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