Cited by 2,751COVID-19 Clinical Research StudiesCOVID-19 diagnosis using AISARS-CoV-2 and COVID-19 Research

The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)

Open access

Chung, Michael, Bernheim, Adam, Mei, Xueyan, Zhang, Ning, Huang, Mingqian, Zeng, Xianjun, Cui, Jiufa, Xu, Wenjian, Yang, Yang, Fayad, Zahi A., Jacobi, Adam, Li, Kunwei, Li, Shaolin, Shan, Hong·2020·Radiology

DOI 10.1148/radiol.2020200230PMC PMC7194022

Open this paper in CoChat — free account

Abstract

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1148/radiol.2020200230

Cites

8 of 8 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Severe Acute Respiratory Syndrome: Radiographic Appearances and Pattern of Progression in 138 Patients
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
Radiologic Pattern of Disease in Patients with Severe Acute Respiratory Syndrome: The Toronto Experience
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
Middle East Respiratory Syndrome-Coronavirus Infection: A Case Report of Serial Computed Tomographic Findings in a Young Male Patient
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

Show all 8 references

Sorted by citations

Items 601–700 of 1,002

Assessment of the Severity of Coronavirus Disease: Quantitative Computed Tomography Parameters versus Semiquantitative Visual Score
Yin, X., Min, X., Nan, Y., Feng, Z., Li, B., Cai, W., Xi, X., & Wang, L. (2020) · Korean Journal of Radiology · doi:10.3348/kjr.2020.0423 · Cited by 36
Characteristics of the Coronavirus Disease 2019 and related Therapeutic Options
Huang, B., Ling, R., Cheng, Y., Wen, J., Dai, Y., Huang, W., Zhang, S., Lu, X., Luo, Y., & Jiang, Y.-Z. (2020) · Molecular Therapy — Methods & Clinical Development · doi:10.1016/j.omtm.2020.06.013 · Cited by 38
Exploration and correlation analysis of changes in Krebs von den Lungen-6 levels in COVID-19 patients with different types in China
Xue, M., Zheng, P., Bian, X., Huang, Z., Huang, H., Zeng, Y., Hu, H., Liu, X., Zhou, L., Sun, B., et al. (2020) · BioScience Trends · doi:10.5582/bst.2020.03197 · Cited by 36
Single Virus Targeting Multiple Organs: What We Know and Where We Are Heading?
Prasad, A., & Prasad, M. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00370 · Cited by 36
Development of a clinical decision support system for the early detection of COVID-19 using deep learning based on chest radiographic images
Qjidaa, M., Benfares, A., Mechbal, Y., Amakdouf, H., Maâroufi, M., Alami, B., & Qjidaa, H. (2020) · International Conference on Intelligent Systems and Computer Vision (ISCV) · doi:10.1109/iscv49265.2020.9204282 · Cited by 36
GraphXCOVID: Explainable deep graph diffusion pseudo-Labelling for identifying COVID-19 on chest X-rays
Avilés-Rivero, A. I., Sellars, P., Schönlieb, C., & Papadakis, N. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108274 · Cited by 39
Inflammatory response in lungs and extrapulmonary sites detected by [18F] fluorodeoxyglucose PET/CT in convalescing COVID-19 patients tested negative for coronavirus
Bai, Y., Xu, J., Chen, L., Fu, C., Kang, Y., Zhang, W., Fakhri, G. E., Gu, J., Shao, F., & Wang, M. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-05083-4 · Cited by 36
Lung Ultrasound: Its Findings and New Applications in Neonatology and Pediatric Diseases
Iovine, E., Nenna, R., Bloise, S., Regina, D. P. L., Pepino, D., Petrarca, L., Frassanito, A., Lubrano, R., & Midulla, F. (2021) · Diagnostics · doi:10.3390/diagnostics11040652 · Cited by 36
Bioengineering of Novel Non-Replicating mRNA (NRM) and Self-Amplifying mRNA (SAM) Vaccine Candidates Against SARS-CoV-2 Using Immunoinformatics Approach
Bhattacharya, M., Sharma, A. R., Ghosh, P., Patra, P., Patra, B. C., Lee, S., & Chakraborty, C. (2022) · Molecular Biotechnology · doi:10.1007/s12033-021-00432-6 · Cited by 36
A deep ensemble learning-based automated detection of COVID-19 using lung CT images and Vision Transformer and ConvNeXt
Tian, G., Wang, Z., Wang, C., Chen, J., Liu, G., Xu, H., Lu, Y., Han, Z., Zhao, Y., Li, Z., et al. (2022) · Frontiers in Microbiology · doi:10.3389/fmicb.2022.1024104 · Cited by 36
Automated Detection Model in Classification of B-Lymphoblast Cells from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique
Ghaderzadeh, M., Hosseini, A., Asadi, F., Abolghasemi, H., Bashash, D., & Roshanpoor, A. (2022) · Scientific Programming · doi:10.1155/2022/4801671 · Cited by 35
High-Dose Convalescent Plasma for Treatment of Severe COVID-19
Santis, G. C. D., de Oliveira, L. C. O., Garibaldi, P. M. M., Almado, C. E. L., Croda, J., Arcanjo, G. G. D. A., Oliveira, É. A. F., Tonacio, A. C., Langhi, D. M., Bordin, J. O., et al. (2022) · Emerging infectious diseases · doi:10.3201/eid2803.212299 · Cited by 35
Consensus Guidelines of Russian Society of Radiology (RSR) and Russian Association of Specialists in Ultrasound Diagnostics in Medicine (RASUDM) «Role of Imaging (X-ray, CT and US) in Diagnosis of COVID-19 Pneumonia» (version 2)
Sinitsyn, V. E., Tyurin, I. E., & Mitkov, V. V. (2020) · Journal of radiology and nuclear medicine · doi:10.20862/0042-4676-2020-101-2-72-89 · Cited by 35
Lobar Distribution of COVID-19 Pneumonia Based on Chest Computed Tomography Findings; A Retrospective Study.
Haseli, S., Khalili, N., Karam, M. B., Taheri, M. S., & Moharamzad, Y. (2020) · Archives of Academic Emergency Medicine · doi:10.22037/aaem.v8i1.665 · Cited by 35
Coronavirus disease 2019 (COVID-19) in Italy: features on chest computed tomography using a structured report system
Grassi, R., Fusco, R., Belfiore, M. P., Montanelli, A., Patelli, G., Urraro, F., Petrillo, A., Granata, V., Sacco, P., Mazzei, M. A., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-73788-5 · Cited by 35
Radiological, epidemiological and clinical patterns of pulmonary viral infections
Stefanidis, K., Konstantelou, E., Yusuf, G. T., Oikonomou, A., Tavernaraki, K., Karakitsos, D., Loukides, S., & Vlahos, I. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109548 · Cited by 35
Biosensors promising bio-device for pandemic screening “COVID-19“
Mobed, A., & Shafigh, E. S. (2021) · Microchemical Journal · doi:10.1016/j.microc.2021.106094 · Cited by 35
A two-stage amplified PZT sensor for monitoring lung and heart sounds in discharged pneumonia patients
Chen, H., Yu, S., Liu, H., Liu, J., Xiao, Y., Wu, D., Pan, X., Zhou, C., Lei, Y., & Liu, S. (2021) · Microsystems & Nanoengineering · doi:10.1038/s41378-021-00274-x · Cited by 35
A multi-scale gated multi-head attention depthwise separable CNN model for recognizing COVID-19
Hong, G., Chen, X., Chen, J., Zhang, M., Ren, Y., & Zhang, X. (2021) · Scientific Reports · doi:10.1038/s41598-021-97428-8 · Cited by 35
The use of lung ultrasound in COVID-19
Blazic, I. M., Cogliati, C. B., Flor, N., Frija, G. G., Kawooya, M., Umbrello, M., Ali, S., Baranne, M., Cho, Y., Pitcher, R., et al. (2022) · ERJ Open Research · doi:10.1183/23120541.00196-2022 · Cited by 35
Prevalence and clinical features of 2019 novel coronavirus disease (COVID-19) in the Fever Clinic of a teaching hospital in Beijing: a single-center, retrospective studyPreprint
Liang, Y., Liang, J.-J., Zhou, Q., Li, X., Lin, F., Deng, Z., Zhang, B., Li, L., Wang, X., Zhu, H., et al. (2020) · medRxiv · doi:10.1101/2020.02.25.20027763 · Cited by 35
CT and COVID-19: Chinese experience and recommendations concerning detection, staging and follow-up
Fan, L., & Liu, S.-Y. (2020) · European Radiology · doi:10.1007/s00330-020-06898-3 · Cited by 34
Correlation of chest CT severity score with clinical parameters in COVID-19 pulmonary disease in a tertiary care hospital in Delhi during the pandemic period
Sharma, S., Aggarwal, A., Sharma, R. K., Patras, E., & Singhal, A. (2022) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-022-00832-x · Cited by 34
COVID-19 diagnosis: A comprehensive review of pre-trained deep learning models based on feature extraction algorithm
Poola, R. G., Lahari, P. L., & Siva Sankar, Y. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101020 · Cited by 36
Use of radiographic features in COVID-19 diagnosis: Challenges and perspectives
Chen, S.-G., Chen, J., Yang, Y.-P., Chien, C., Wang, M., & Lin, L. (2020) · Journal of the Chinese Medical Association · doi:10.1097/jcma.0000000000000336 · Cited by 34
Self-citation
COVID-19 Assessment with Bedside Lung Ultrasound in a Population of Intensive Care Patients Treated with Mechanical Ventilation and ECMO
Møller‐Sørensen, H., Gjedsted, J., Jørgensen, V. L., & Hansen, K. L. (2020) · Diagnostics · doi:10.3390/diagnostics10070447 · Cited by 34
Risk factors for intensive care unit admission and mortality in hospitalized COVID-19 patients
Ayaz, A., Arshad, A., Malik, H., Ali, H., Hussain, E., & Jamil, B. (2020) · Acute and Critical Care · doi:10.4266/acc.2020.00381 · Cited by 37
Recommendations for treatment of critically ill patients with COVID-19
Kluge, S., Janssens, U., Welte, T., Weber‐Carstens, S., Schälte, G., Salzberger, B., Gastmeier, P., Länger, F. P., Welper, M., Westhoff, M., et al. (2020) · Der Anaesthesist · doi:10.1007/s00101-020-00879-3 · Cited by 35
Meltblown fabric vs nanofiber membrane, which is better for fabricating personal protective equipments
Wu, J., Zhou, H., Zhou, J., Zhu, X., Zhang, B., Feng, S., Zhong, Z., Kong, L., & Xing, W. (2020) · Chinese Journal of Chemical Engineering · doi:10.1016/j.cjche.2020.10.022 · Cited by 34
Correlation between CT findings and outcomes in 46 patients with coronavirus disease 2019
Lei, Q., Li, G., Ma, X., Tian, J., Wu, Y., Chen, H., Xu, W., Li, C., & Jiang, G. (2021) · Scientific Reports · doi:10.1038/s41598-020-79183-4 · Cited by 34
Self-citation
Diagnosis of COVID-19 Pneumonia Based on Graph Convolutional Network
Liang, X., Zhang, Y., Wang, J., Ye, Q., Liu, Y., & Tong, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2020.612962 · Cited by 35
Targeted pulmonary drug delivery in coronavirus disease (COVID-19) therapy: A patient-specific in silico study based on magnetic nanoparticles-coated microcarriers adhesion
Ebrahimi, S., Shamloo, A., Alishiri, M., Mofrad, Y. M., & Akherati, F. (2021) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2021.121133 · Cited by 34
COVID-19 pneumonia: A review of typical radiological characteristics
Arróspide, M. C., Martínez-Besteiro, E., Couñago, F., & Landete, P. (2021) · World Journal of Radiology · doi:10.4329/wjr.v13.i10.327 · Cited by 34
Automatic classification of severity of COVID ‐19 patients using texture feature and random forest based on computed tomography images
Amini, N., & Shalbaf, A. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22679 · Cited by 34
Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-Ray Images
Chaddad, A., Hassan, L., & Desrosiers, C. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3119071 · Cited by 34
Hallazgos iniciales en la radiografía de tórax como predictores de empeoramiento en la infección pulmonar por SARS-CoV-2. Correlación en 265 pacientes
Felipe, D. P., Campos, M. I. R., Espinosa, J. S. M., Rubio, Y. M., Quan, J. C. F., & Revilla, M. V. C. (2021) · Radiología · doi:10.1016/j.rx.2021.03.004 · Cited by 33
The Role of Imaging Techniques in Management of COVID-19 in China: From Diagnosis to Monitoring and Follow-Up
Jiang, Z.-Z., He, C., Wang, D., Shen, H.-L., Sun, J.-L., Gan, W.-N., Lu, J.-Y., & Liu, X. (2020) · Medical Science Monitor · doi:10.12659/msm.924582 · Cited by 33
Chest computed tomography for the diagnosis of patients with coronavirus disease 2019 (COVID-19): a rapid review and meta-analysis
on behalf of COVID-19 Evidence and Recommendations Working Group, Lv, M., Wang, M., Yang, N., Luo, X., Li, W., Chen, X., Liu, Y., Ren, M., Zhang, X., et al. (2020) · Annals of Translational Medicine · doi:10.21037/atm-20-3311 · Cited by 33
Large pulmonary cavity in COVID-19 cured patient case report
Chen, Y., Chen, W., Zhou, J., Sun, C., & Lei, Y. (2020) · Annals of Palliative Medicine · doi:10.21037/apm-20-452 · Cited by 33
Potential Drugs and Remedies for the Treatment of COVID-19: a Critical Review
Barati, F., Pouresmaieli, M., Ekrami, E., Asghari, S., Ziarani, F. R., & Mamoudifard, M. (2020) · Biological Procedures Online · doi:10.1186/s12575-020-00129-1 · Cited by 33
Novel coronavirus disease 2019 (COVID-19) outbreak in children in Iran: Atypical CT manifestations and mortality risk of severe COVID-19 infection
Mamishi, S., Heydari, H., Aziz‐Ahari, A., Shokrollahi, M. R., Pourakbari, B., Mahmoudi, S., & Movahedi, Z. (2020) · Journal of Microbiology Immunology and Infection · doi:10.1016/j.jmii.2020.07.019 · Cited by 33
COVID-19 disease: CT Pneumonia Analysis prototype by using artificial intelligence, predicting the disease severity
Gouda, W., & Yasin, R. (2020) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-020-00309-9 · Cited by 33
Nanomaterials-Based Biosensors for COVID-19 Detection—A Review
Sharma, S., Saini, S., Khangembam, M., & Singh, V. (2020) · IEEE Sensors Journal · doi:10.1109/jsen.2020.3036748 · Cited by 33
Visceral adipose tissue area predicts intensive care unit admission in COVID-19 patients
Pediconi, F., Rizzo, V., Schiaffino, S., Cozzi, A., Pepa, G. D., Galati, F., Catalano, C., & Sardanelli, F. (2020) · Obesity Research & Clinical Practice · doi:10.1016/j.orcp.2020.12.002 · Cited by 33
COVID-19–associated Nephropathy Includes Tubular Necrosis and Capillary Congestion, with Evidence of SARS-CoV-2 in the Nephron
Bouquegneau, A., Erpicum, P., Grosch, S., Habran, L., Hougrand, O., Huart, J., Krzesinski, J.-M., Misset, B., Hayette, M., Delvenne, P., et al. (2021) · Kidney360 · doi:10.34067/kid.0006992020 · Cited by 37
Brain and Lung Imaging Correlation in Patients with COVID-19: Could the Severity of Lung Disease Reflect the Prevalence of Acute Abnormalities on Neuroimaging? A Global Multicenter Observational Study
Mahammedi, A., Ramos, A., Bargalló, N., Gaskill, M. F., Kapur, S., Saba, L., Carrete, H., Sengupta, S., Álvarez, E. S., Hilario, A., et al. (2021) · American Journal of Neuroradiology · doi:10.3174/ajnr.a7072 · Cited by 33
CO-RADS versus CT-SS scores in predicting severe COVID-19 patients: retrospective comparative study
Zayed, N. E., Bessar, M. A., & Lotfy, S. M. (2021) · Egyptian Journal of Bronchology · doi:10.1186/s43168-021-00060-3 · Cited by 33
Pleural abnormalities in COVID-19: a narrative review
Saha, B. K., Chong, W. H., Austin, A., Kathuria, R., Datar, P. B., Shkolnik, B., Beegle, S. H., & Chopra, A. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-21-542 · Cited by 33
Quantitative Analysis of Pleural Line and B-Lines in Lung Ultrasound Images for Severity Assessment of COVID-19 Pneumonia
Wang, Y., Zhang, Y., He, Q., Liao, H., & Luo, J. (2021) · IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control · doi:10.1109/tuffc.2021.3107598 · Cited by 34
Pneumocystis jirovecii Pneumonia in Non-HIV Patients Recovering from COVID-19: A Single-Center Experience
Gentile, I., Viceconte, G., Lanzardo, A., Zotta, I., Zappulo, E., Pinchera, B., Scotto, R., Moriello, N. S., Foggia, M., Giaccone, A., et al. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182111399 · Cited by 33
SC2Net: A Novel Segmentation-Based Classification Network for Detection of COVID-19 in Chest X-Ray Images
Zhao, H., Fang, Z., Ren, J., MacLellan, C., Xia, Y., Li, S., Sun, M., & Ren, K. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3177854 · Cited by 33
Self-citation
Deep Learning-Aided Automated Pneumonia Detection and Classification Using CXR Scans
Jain, D. K., Singh, T., Saurabh, P., Bisen, D., Sahu, N., Mishra, J., & Rahman, M. H. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7474304 · Cited by 33
Severe Acute Respiratory Disease in a Huanan Seafood Market Worker: Images of an Early Casualty
Qian, L., Yu, J., & Shi, H. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200033 · Cited by 32
A novel lightweight deep learning model based on SqueezeNet architecture for viral lung disease classification in X-ray and CT images
Agnihotri, A., & Kohli, N. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.425 · Cited by 32
La radiología en el diagnóstico de la neumonía por SARS-CoV-2 (COVID-19)
Sánchez-Oro, R., Nuez, J. P. T., & Martínez-Sanz, G. (2020) · Medicina Clínica · doi:10.1016/j.medcli.2020.03.004 · Cited by 32
Computed Tomography Features of Coronavirus Disease 2019 (COVID-19)
Goyal, N., Chung, M., Bernheim, A., Keir, G., Mei, X., Huang, M., Li, S., & Kanne, J. P. (2020) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000527 · Cited by 32
Self-citation
Can CT performed in the early disease phase predict outcome of patients with COVID 19 pneumonia? Analysis of a cohort of 64 patients from Germany
Meiler, S., Schaible, J., Poschenrieder, F., Scharf, G., Zeman, F., Rennert, J., Pregler, B., Kleine, H., Stroszczynski, C., Zorger, N., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109256 · Cited by 32
Implementation of the Radiological Society of North America Expert Consensus Guidelines on Reporting Chest CT Findings Related to COVID-19: A Multireader Performance Study
Som, A., Lang, M., Yeung, T., Carey, D. E., Garrana, S. H., Mendoza, D. P., Flores, E. J., Li, M., Sharma, A., McDermott, S., et al. (2020) · Radiology Cardiothoracic Imaging · doi:10.1148/ryct.2020200276 · Cited by 32
Comparison Between Lung Ultrasound and Computed Tomographic Findings in Patients With COVID ‐19 Pneumonia
Lopes, A. J., Mafort, T. T., da Costa, C. H., Rufino, R., Firmida, M. D. C., Kirk, K. M., Chantong, C. G. C., da Costa, H. D. S. B., da Cruz, C. M. B. Q., & Mogami, R. (2020) · Journal of Ultrasound in Medicine · doi:10.1002/jum.15521 · Cited by 32
Association between laboratory parameters and CT severity in patients infected with Covid-19: A retrospective, observational study
Yılmaz, A., Sabırlı, R., Seyit, M., Özen, M., Oskay, A., Çakmak, V., Gören, T., & Türkçüer, İ. (2021) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2021.01.040 · Cited by 32
Role of High Resolution Computed Tomography chest in the diagnosis and evaluation of COVID -19 patients -A systematic review and meta-analysis
Ishfaq, A., Farooq, S. M. Y., Goraya, A., Yousaf, M., Gilani, S. A., Kiran, A., Ayoub, M., Akhter, J., & Bacha, R. (2021) · European Journal of Radiology Open · doi:10.1016/j.ejro.2021.100350 · Cited by 32
Multi-Modal Point-of-Care Diagnostics for COVID-19 Based on Acoustics and Symptoms
Chetupalli, S. R., Krishnan, P., Sharma, N. K., Muguli, A., Kumar, R., Nanda, V., Pinto, L., Ghosh, P., & Ganapathy, S. (2023) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2023.3250700 · Cited by 36
DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images
Qi, S., Xu, C., Li, C., Tian, B., Xia, S., Ren, J., Yang, L., Wang, H., & Yu, H. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106406 · Cited by 33
Lightweight Neural Network for COVID-19 Detection from Chest X-ray Images Implemented on an Embedded System
Sanida, T., Sideris, A., Tsiktsiris, D., & Dasygenis, M. (2022) · Technologies · doi:10.3390/technologies10020037 · Cited by 32
Key elements of preparedness for pandemic coronavirus disease 2019 (COVID-19) in nuclear medicine units
Assadi, M., Gholamrezanezhad, A., Jokar, N., Keshavarz, M., Picchio, M., Seregni, E. C., Bombardieri, E., & Chiti, A. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-04780-4 · Cited by 32
The role of chest computed tomography in the management of COVID-19: A review of results and recommendations
Wong, M. D., Thai, T., Li, Y., & Liu, H. (2020) · Experimental Biology and Medicine · doi:10.1177/1535370220938315 · Cited by 31
Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study
Yu, Y., Wang, X., Li, M., Gu, L., Xie, Z., Gu, W., Xu, F., Bao, Y., Liu, R., Hu, S., et al. (2020) · BMC Medical Imaging · doi:10.1186/s12880-020-00513-z · Cited by 31
Radiological signs of a new coronavirus infection COVID-19
Speranskaya, A. A. (2020) · Diagnostic radiology and radiotherapy · doi:10.22328/2079-5343-2020-11-1-18-25 · Cited by 31
Clinico-Radiological Features and Outcomes in Pregnant Women with COVID-19 Pneumonia Compared with Age-Matched Non-Pregnant Women
Liu, F., Liu, H., Hou, L., Li, J., Zheng, H. W., Chi, R., Lan, W., & Wang, D. (2020) · Infection and Drug Resistance · doi:10.2147/idr.s264541 · Cited by 31
Overview of COVID ‐19 testing and implications for otolaryngologists
Vinh, D., Zhao, X., Kiong, K. L., Guo, T. W., Jozaghi, Y., Yao, C., Kelley, J. M., & Hanna, E. Y. (2020) · Head & Neck · doi:10.1002/hed.26213 · Cited by 31
Target-shaped combined halo and reversed-halo sign, an atypical chest CT finding in COVID-19
Shaghaghi, S., Daskareh, M., Irannejad, M., Shaghaghi, M., & Kamel, I. R. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.06.038 · Cited by 31
Admission fasting blood glucose predicts 30‐day poor outcome in patients hospitalized for COVID ‐19 pneumonia
Zhang, B., Liu, S., Zhang, L., Dong, Y., & Zhang, S. (2020) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14132 · Cited by 31
The bullseye sign: A variant of the reverse halo sign in COVID-19 pneumonia
McLaren, T. A., Gruden, J. F., & Green, D. B. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.07.024 · Cited by 31
Discrimination of pulmonary ground-glass opacity changes in COVID‐19 and non-COVID-19 patients using CT radiomics analysis
Xie, C., Ng, M., Ding, J., Leung, S. T., Lo, C. S. Y., Wong, H. Y. F., & Vardhanabhuti, V. (2020) · European Journal of Radiology Open · doi:10.1016/j.ejro.2020.100271 · Cited by 31
The mean severity score and its correlation with common computed tomography chest manifestations in Egyptian patients with COVID-2019 pneumonia
Hafez, M. A. F. (2020) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-020-00368-y · Cited by 31
Unsupervised domain adaptation based COVID-19 CT infection segmentation network
Chen, H., Jiang, Y., Loew, M., & Ko, H. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02691-x · Cited by 31
Pulmonary function tests in the follow-up of children with COVID-19
Öztürk, G. K., Beken, B., Doğan, S., & Akar, H. H. (2022) · European Journal of Pediatrics · doi:10.1007/s00431-022-04493-w · Cited by 33
Deep Learning Algorithm Trained with COVID-19 Pneumonia Also Identifies Immune Checkpoint Inhibitor Therapy-Related Pneumonitis
Mallio, C. A., Napolitano, A., Castiello, G., Giordano, F. M., D'Alessio, P., Iozzino, M., Sun, Y., Angeletti, S., Russano, M., Santini, D., et al. (2021) · Cancers · doi:10.3390/cancers13040652 · Cited by 30
A Novel Technique for Early Detection of COVID-19
Yamin, M., Sen, A. A. A., Al-Kubaisy, Z. M., & Almarzouki, R. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.017433 · Cited by 32
Management of Covid-19 Detection Using Artificial Intelligence in 2020 Pandemic
Ghaderzadeh, M., & Aria, M. (2021) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3472813.3472820 · Cited by 30
A retrospective study on the epidemiological characteristics and establishment of an early warning system of severe COVID‐19 patients
Yang, P. C., Wang, P., Song, Y., Zhang, A., Yuan, G., & Cui, Y. (2020) · Journal of Medical Virology · doi:10.1002/jmv.26022 · Cited by 30
Clinical and imaging features predict mortality in COVID-19 infection in Iran
Homayounieh, F., Zhang, E. W., Babaei, R., Mobin, H. K., Sharifian, M., Mohseni, I., Kuo, A. H., Arru, C. D., Kalra, M. K., & Digumarthy, S. R. (2020) · PLoS ONE · doi:10.1371/journal.pone.0239519 · Cited by 30
Correlation between age, sex, and severity of Coronavirus disease-19 based on chest computed tomography severity scoring system
Farghaly, S., & Makboul, M. (2021) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-021-00408-1 · Cited by 30
A review on current diagnostic techniques for COVID-19
Jaddaoui, I. E., Allali, M., Raoui, S., Sehli, S., Habib, N., Chaouni, B., Idrissi, N. A., Benslima, N., Maher, W., Benrahma, H., et al. (2021) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2021.1886927 · Cited by 31
Texture feature-based machine learning classifier could assist in the diagnosis of COVID-19
Wu, Z., Li, L., Jin, R., Liang, L., Hu, Z., Tao, L., Han, Y. K., Feng, W., Zhou, D., Li, W., et al. (2021) · European Journal of Radiology · doi:10.1016/j.ejrad.2021.109602 · Cited by 30
Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review
Montazeri, M., ZahediNasab, R., Farahani, A., Mohseni, H., & Ghasemian, F. (2021) · JMIR Medical Informatics · doi:10.2196/25181 · Cited by 31
Comparison of Chest CT Findings of COVID-19, Influenza, and Organizing Pneumonia: A Multireader Study
Garrana, S. H., Som, A., Ndakwah, G. S., Yeung, T., Febbo, J., Heeger, A. P., Lang, M., McDermott, S., Mendoza, D. P., Zhang, E. W., et al. (2021) · American Journal of Roentgenology · doi:10.2214/ajr.21.25640 · Cited by 30
Proteomics and metabonomics analyses of Covid-19 complications in patients with pulmonary fibrosis
Yang, J., Chen, C., Chen, W., Huang, L., Fu, Z., Ye, K., Lv, L., Nong, Z., Zhou, X., Lu, W., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-94256-8 · Cited by 29
COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network
Monday, H. N., Li, J., Nneji, G. U., Hossin, M. A., Kumar, R., Oluwasanmi, A., James, E. C., Mgbejime, G. T., Umana, E. S., Chikwendu, I. A., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030741 · Cited by 30
A Community-transmitted Case of Severe Acute Respiratory Distress Syndrome (SARS) Due to SARS-CoV-2 in the United States
Sanville, B. C., Corbett, R., Pidcock, W., Hardin, K., Sebat, C. M., Nguyen, M.-V. H., Thompson, G. R., Haczku, A., Schivo, M., & Cohen, S. (2020) · Clinical Infectious Diseases · doi:10.1093/cid/ciaa347 · Cited by 29
Tracheal trauma after difficult airway management in morbidly obese patients with COVID-19
Arab, O. A., Huette, P., Berna, P., & Mahjoub, Y. (2020) · British Journal of Anaesthesia · doi:10.1016/j.bja.2020.04.004 · Cited by 29
Empfehlungen zur intensivmedizinischen Therapie von Patienten mit COVID-19 – 3. Version
Kluge, S., Janssens, U., Welte, T., Weber‐Carstens, S., Schälte, G., Salzberger, B., Gastmeier, P., Länger, F. P., Wepler, M., Westhoff, M., et al. (2020) · Der Anaesthesist · doi:10.1007/s00101-020-00833-3 · Cited by 29
Development and clinical implementation of tailored image analysis tools for COVID-19 in the midst of the pandemic: The synergetic effect of an open, clinically embedded software development platform and machine learning
Anastasopoulos, C., Weikert, T., Yang, S., Abdulkadir, A., Schmülling, L., Bühler, C., Paciolla, F., Sexauer, R., Cyriac, J., Nešić, I., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109233 · Cited by 29
Coronavirus global pandemic: An overview of current findings among pediatric patients
Perikleous, E. P., Tsalkidis, A., Bush, A., & Paraskakis, E. (2020) · Pediatric Pulmonology · doi:10.1002/ppul.25087 · Cited by 29
The study of automatic machine learning base on radiomics of non-focus area in the first chest CT of different clinical types of COVID-19 pneumonia
Tan, H., Xiong, F., Jiang, Y., Huang, W., Wang, Y., Li, H., You, T., Fu, T., Lu, R., & Peng, B. (2020) · Scientific Reports · doi:10.1038/s41598-020-76141-y · Cited by 29
Spectrum of atypical pulmonary manifestations of COVID-19 on computed tomography
Gurumurthy, B., Das, S. K., Hiremath, R. S., Shetty, S. P., Hiremath, A. D., & GOWDA, T. (2021) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-021-00448-7 · Cited by 29
COVID-19 in Children
Kalyanaraman, M., & Anderson, M. R. (2022) · Pediatric Clinics of North America · doi:10.1016/j.pcl.2022.01.013 · Cited by 30
Computed tomography scan in COVID-19: a systematic review and meta-analysis
Sharif, P. M., Nematizadeh, M., Saghazadeh, M., Saghazadeh, A., & Rezaei, N. (2022) · Polish Journal of Radiology · doi:10.5114/pjr.2022.112613 · Cited by 29
Residual Dense Attention Networks for COVID-19 Computed Tomography Images Super Resolution
Qiu, D., Cheng, Y., & Wang, X. (2022) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2022.3193121 · Cited by 29
Altered intrinsic brain activity and functional connectivity in COVID-19 hospitalized patients at 6-month follow-up
Li, R., Liu, G., Zhang, X., Zhang, M., Lu, J., & Li, H. (2023) · BMC Infectious Diseases · doi:10.1186/s12879-023-08331-8 · Cited by 30

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: Chung, Michael
Bernheim, Adam
Mei, Xueyan
Zhang, Ning
Huang, Mingqian
Zeng, Xianjun
Cui, Jiufa
Xu, Wenjian
Yang, Yang
Fayad, Zahi A.
Jacobi, Adam
Li, Kunwei
Li, Shaolin
Shan, Hong
Venue: Radiology
Publisher: Radiological Society of North America (RSNA)
Published: 2020-04
Volume: 295
Issue: 1
Pages: 202-207
ISSN: 0033-8419 (ISSN-L)
1527-1315
DOI: 10.1148/radiol.2020200230
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks