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Chest CT for Typical Coronavirus Disease 2019 (COVID-19) Pneumonia: Relationship to Negative RT-PCR Testing
Open accessAbstract
Some patients with positive chest CT findings may present with negative results of real-time reverse-transcription polymerase chain reaction (RT-PCR) tests for coronavirus disease 2019 (COVID-19). In this study, the authors present chest CT findings from five patients with COVID-19 infection who had initial negative RT-PCR results. All five patients had typical imaging findings, including ground-glass opacity (five patients) and/or mixed ground-glass opacity and mixed consolidation (two patients). After isolation for presumed COVID-19 pneumonia, all patients were eventually confirmed to have COVID-19 infection by means of repeated swab tests. A combination of repeated swab tests and CT scanning may be helpful for individuals with a high clinical suspicion of COVID-19 infection but negative findings at RT-PCR screening.
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“There are currently a number of different ways to detect COVID-19, whereas computed tomography and X-rays remain the major surgical techniques [80–82].”
From A comprehensive survey on federated learning techniques for healthcare informatics (2023) · cites this paper as [81]
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A novel coronavirus from patients with pneumonia in China, 2019
Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., Zhao, X., Huang, B., Shi, W., Lu, R., et al. (2020) · New England Journal of Medicine · doi:10.1056/nejmoa2001017 · Cited by 28,244
CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
Chung, M., Bernheim, A., Mei, X., Zhang, N., Huang, M., Zeng, X., Cui, J., Xu, W., Yang, Y., Fayad, Z. A., et al. (2020) · Radiology · doi:10.1148/radiol.2020200230 · Cited by 2,751
Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia
Song, F., Shi, N., Shan, F., Zhang, Z., Shen, J., Lu, H., Ling, Y., Jiang, Y., & Shi, Y. (2020) · Radiology · doi:10.1148/radiol.2020200274 · Cited by 1,217
Kanne, J. P. (2020) · Radiology · doi:10.1148/radiol.2020200241 · Cited by 668
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
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
Cited by
Sorted by citations
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Patients with Initial Negative RT-PCR and Typical Imaging of COVID-19: Clinical Implications
Baicry, F., Borgne, P. L., Fabacher, T., Behr, M., Lemaitre, E. L., Gayol, P., Harscoat, S., Issur, N., Kepka, S., Ohana, M., et al. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9093014 · Cited by 15
Chen, X., Zhang, G., Shuai-ying, H., Bai, L., & Lu, J. (2020) · Chinese Medical Sciences Journal · doi:10.24920/003727 · Cited by 15
“After evaluation using inclusion and exclusion criteria, a total of 24 publications were included in the study (Figure 1).[3-13, 19, 21-32] The characteristics of the 24 studies are”
MethodImaging of lung pathology in COVID-19 (literature review and own data)
Tyurin, I. E., & Strutynskaya, A. D. (2020) · PULMONOLOGIYA · doi:10.18093/0869-0189-2020-30-5-658-670 · Cited by 17
Does CT help in reducing RT-PCR false negative rate for COVID-19?
Kohli, A., Joshi, A. R., Shah, A., Jain, R. D., Gorlawar, A., Dhapare, A., Desai, J., Shetty, A., Shah, C., Ostwal, P., et al. (2021) · Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging · doi:10.4103/ijri.ijri_739_20 · Cited by 15
Elmokadem, A. H., Batouty, N. M., Bayoumi, D., Gadelhak, B., Wahab, R. M. A., Zaky, M. M., Abo-Hedibah, S. A., Ehab, A., & Elmorsy, A. (2021) · Insights into Imaging · doi:10.1186/s13244-020-00956-6 · Cited by 15
Albano, D., Bertagna, F., Alongi, P., Baldari, S., Baldoncini, A., Bartolomei, M., Boccaletto, F., Boero, M., Borsatti, E., Bruno, A., et al. (2021) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-021-05219-0 · Cited by 15
“For this reason, some authors suggest considering the combination of clinical, imaging, and laboratory reports to make the final diagnosis, and in some cases, the CT morphological pattern seems to be better than the RT-PCR test [8, 9].”
BackgroundComputed tomography of the chest in patients with COVID-19: what do radiologists want to know?
Razek, A. A. K. A., Fouda, N., Fahmy, D. M., tantawy, M., Sultan, A., Bilal, M., Zaki, M. E. S., Abdel-Aziz, M., & Sobh, D. M. (2021) · Polish Journal of Radiology · doi:10.5114/pjr.2021.104049 · Cited by 15
Yavuz, B. G., Çolak, Ş., Güven, R., Altundağ, İ., Seyhan, A. U., & Inanc, R. G. (2021) · Gerontology · doi:10.1159/000514481 · Cited by 15
Zarei, F., Jalli, R., Iranpour, P., Sefidbakht, S., Soltanabadi, S., Rezaee, M., Jahankhah, R., & Manafi, A. (2021) · Academic Radiology · doi:10.1016/j.acra.2021.04.010 · Cited by 15
“010 transcriptase-polymerase chain reaction (RT-PCR) is the reference standard tool to confirm the diagnosis of COVID-19 pneumonia, a large number of false-negative/positive RTPCR results have recently been observed (10,11), making the diagnosis rather challenging.”
BackgroundMethodGupta, S., Panwar, A., Gupta, S., Manwal, M., & Aeri, M. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82469-3_22 · Cited by 15
Deep Residual Neural Network for COVID-19 Detection from Chest X-ray Images
Panahi, A., Moghadam, R. A., Akrami, M., & Madani, K. (2022) · SN Computer Science · doi:10.1007/s42979-022-01067-3 · Cited by 15
COVID-19 prognosis using limited chest X-ray images
Mondal, A. K. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.108867 · Cited by 15
“With these modalities, researchers have oberved that COVID-19 patients’ lungs exhibit ground-glass opacity nd/or mixed ground-glass opacity and mixed consolidation that an separate COVID-19-positive cases from COVID-19-negative ases [7,8].”
A novel deep learning-based method for COVID-19 pneumonia detection from CT images
Luo, J., Sun, Y., Chi, J., Liao, X., & Xu, C. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-02022-1 · Cited by 16
Liu, X., Lv, J., Gan, L., Zhang, Y., Sun, F., Meng, B., Jheon, A. H., Yan, F., Li, B., Zhou, X., et al. (2020) · Indian Journal of Medical Microbiology · doi:10.4103/ijmm.ijmm_20_133 · Cited by 15
“It could be seen from our study that 30% of the patients with laboratory-confirmed COVID-19 were admitted to the hospital due to positive chest CT scans, suggesting chest CT has certain sensitivity and specificity thus an important means of diagnosis for COVID-19, which has also been confirmed in some studies.[17-19]…”
Miao, C., Zhuang, J., Jin, M., Xiong, H., Huang, P., Zhao, Q., Li, M., Du, J., Yang, X., Huang, P., et al. (2020) · medRxiv · doi:10.1101/2020.03.22.20040782 · Cited by 15
“COVID-19 is mainly viral nucleic acid reverse-transcription polymerase-chain-reaction (RT-PCR) testing, but considering its limited production, time consuming and relatively high false negative rate(5, 6), it might not effectively control the spread.”
BackgroundA Systematic Meta-Analysis of CT Features of COVID-19: Lessons from RadiologyPreprint
Venugopal, V. K., Mahajan, V., Rajan, S., Agarwal, V., Rajan, R., Syed, S., & Mahajan, H. (2020) · medRxiv · doi:10.1101/2020.04.04.20052241 · Cited by 15
Adams, H. J. A., Kwee, T. C., & Kwee, R. M. (2020) · Radiology · doi:10.1148/radiol.2020201709 · Cited by 15
“Furthermore, we conducted an additional subgroup analysis for the studies with a low risk of bias for the CT interpretation, which clarified that the image readers were blinded to the clinical information or used radiology reports obtained from the routine clinical practice (2, 7-29).”
Imaging evaluation of COVID-19 in the emergency department
Pakray, A., Walker, D., Figacz, A., Kilanowski, S., Rhodes, C., Doshi, S., & Coffey, M. P. (2020) · Emergency Radiology · doi:10.1007/s10140-020-01787-0 · Cited by 14
Simoni, P., Bazzocchi, A., Boitsios, G., Leucio, A. D., Preziosi, M., & Gómez, M. P. A. (2020) · Journal of Medical Imaging and Radiation Oncology · doi:10.1111/1754-9485.13098 · Cited by 14
“In the most extensive published series of paediatric patients with COVID-19, including 2143 Chinese children, 95% were asymptomatic or showed mild-to-moderate symptoms.6 In Shenzhen, China, SARS-CoV-2 reported infection prevalence in children under ten years was 7.4%, similar to that of the general population (7.9%).7…”
BackgroundInfluentialGüler, E., Ünal, N. G., Çinkooğlu, A., Savaş, R., Köse, T., Pullukçu, H., Harman, M., Elmas, N., Ramaiya, N. H., & Özütemiz, A. Ö. (2020) · Abdominal Radiology · doi:10.1007/s00261-020-02805-y · Cited by 14
From Radiological Manifestations to Pulmonary Pathogenesis of COVID-19: A Bench to Bedside Review
Saburi, A., Schoepf, U. J., Ulversoy, K. A., Jafari, R., Eghbal, F., & Ghanei, M. (2020) · Radiology Research and Practice · doi:10.1155/2020/8825761 · Cited by 14
Peroni, L. A., Toscaro, J. M., Canateli, C., Tonoli, C. C. C., de Olivera, R. R., Benedetti, C. E., Coimbra, L. D., Borin, A., Marques, R. E., Proença‐Módena, J. L., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.635701 · Cited by 14
Sanctis, V. D., Ruggiero, L., Soliman, A. T., Daar, S., Maio, S. D., & KATTAMIS, C. A. (2020) · Acta bio-medica dell'Ateneo parmense/Acta biomedica · doi:10.23750/abm.v91i2.9543 · Cited by 14
When cancer encounters COVID-19 in China: what have we suffered, experienced and learned
Chen, H., Wu, X., Wang, W., & Wang, Q. (2020) · Japanese Journal of Clinical Oncology · doi:10.1093/jjco/hyaa077 · Cited by 14
“In most cases, concordant CT scan and PCR test results were used to diagnose COVID-19 (38).”
MethodKicking on while it’s still kicking off – getting surgery and anaesthesia restarted after COVID‐19
Cook, T. M., & Harrop-Griffiths, W. A. (2020) · Anaesthesia · doi:10.1111/anae.15128 · Cited by 14
Xie, S., Lei, Z., Chen, X., Liu, W., Wang, X., Dong, Y., Guo, Y., Duan, Y., Cao, H., Qin, J., et al. (2020) · British Journal of Radiology · doi:10.1259/bjr.20200243 · Cited by 14
“7–10 In all 28 patients, the initial CT findings were evaluated for the following features: (1) type of opacity: pure/mixed GGO or consolidation; (2) lesion location: peripheral pulmonary/ subpleural or central distribution/scattered; (3) morphology of lesions: large irregular/patchy opacities, rounded opacities or li…”
BackgroundMarando, M., Tamburello, A., & Gianella, P. (2020) · European Journal of Case Reports in Internal Medicine · doi:10.12890/2020_001680 · Cited by 14
“The chest CT abnormalities seen in our patient have also been identified in patients before the detection of viral RNA from upper respiratory specimens [9].”
BackgroundAzadbakht, J., Haghi‐Aminjan, H., & Farhood, B. (2020) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-020-00261-8 · Cited by 14
“Moreover, due to the limitation of sampling materials in the early stage of the infection, the positive rate is relatively low [10].”
BackgroundInfluentialAlharthy, A. M., Bakirova, G., Bakheet, H., Balhamar, A., Brindley, P. G., Alqahtani, S. A., Memish, Z. A., & Karakitsos, D. (2020) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2020.12.019 · Cited by 14
Segmentation of COVID-19 pneumonia lesions: A deep learning approach
Ghomi, Z., Mirshahi, R., Bagheri, A. K., Fattahpour, A., Mohammadiun, S., Gharahbagh, A. A., Djavadifar, A., Arabalibeik, H., Sadiq, R., & Hewage, K. (2020) · Medical Journal of the Islamic Republic of Iran · doi:10.47176/mjiri.34.174 · Cited by 14
“A CT involvement score has been proposed by Xie et al for the classification of lung involvement (3) and assessment of disease extent and severity.”
BackgroundMethodAnees, M., Ameer, H., Haider, S. A., & Ali, I. (2021) · Allergologia et Immunopathologia · doi:10.15586/aei.v49i1.60 · Cited by 14
van Dam, P. M. E. L., Zelis, N., Stassen, P. M., van Twist, D. J. L., de Leeuw, P. W., van Kuijk, S. M. J., & Buijs, J. (2021) · BMJ Open · doi:10.1136/bmjopen-2020-045141 · Cited by 14
Demirci, N. Y., Dikmen, A. U., Taşçı, C., Doğan, D., Arslan, Y., Öcal, N., Taşar, M., Bozlar, U., Artuk, C., Yılmaz, G., et al. (2021) · International Journal of Clinical Practice · doi:10.1111/ijcp.14459 · Cited by 14
O’Donnell, C., Iles, L. E., & Woodford, N. W. F. (2021) · Forensic Science Medicine and Pathology · doi:10.1007/s12024-021-00389-7 · Cited by 14
Zandi, M., Farahani, A. S., Zakeri, A., Rezayat, S. A., Mohammadi, R., Das, U., Dimmock, J. R., Afzali, S., Nakhaei, M. A., Doroudi, A., et al. (2021) · International Journal of Microbiology · doi:10.1155/2021/5528786 · Cited by 14
“Second, low viral load and laboratory errors might be playing a key role [54].”
BackgroundKoch, V., Gruenewald, L. D., Albrecht, M. H., Eichler, K., Gruber‐Rouh, T., Yel, İ., Alizadeh, L. S., Mahmoudi, S., Scholtz, J., Martin, S. S., et al. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.02.019 · Cited by 14
Convolutional neural network for the detection of coronavirus based on X-ray images
Ahmed, E. H., Alsemawi, M. R. M., Mutar, M. H., Hanoosh, H. O., & Abbas, A. H. (2022) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v26.i1.pp37-45 · Cited by 14
Li, J., Luo, X., Ma, H., & Zhao, W. (2022) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2022.3216661 · Cited by 14
Self-citationA COVID-19 patient with seven consecutive false-negative rRT-PCR results from sputum specimens
Song, C.-Y., Yang, D.-G., & Lu, Y. (2020) · Internal and Emergency Medicine · doi:10.1007/s11739-020-02423-y · Cited by 14
Carcinoembryonic Antigen: A Potential Biomarker to Evaluate the Severity and Prognosis of COVID-19
Chen, Q., Kong, H., Qi, X., Ding, W., Ji, N., Wu, C., Huang, C., Wu, W., Huang, M., Xie, W., et al. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.579543 · Cited by 14
Submillisievert chest CT in patients with COVID-19 - experiences of a German Level-I center
Hamper, C. M., Fleckenstein, F. N., Büttner, L., Hamm, B., Thieme, N., Thieß, H.-M., Scholz, O., Döllinger, F., & Böning, G. (2020) · European Journal of Radiology Open · doi:10.1016/j.ejro.2020.100283 · Cited by 14
“In the course of the disease, CT can show these typical changes, even if no virus is detected in the nasopharyngeal or throat swab [14,15].”
Value of minimum intensity projections for chest CT in COVID-19 patients
Booz, C., Vogl, T. J., Schoepf, U. J., Caruso, D., Inserra, M. C., Yel, İ., Martin, S. S., Bucher, A. M., Lenga, L., Caudo, D., et al. (2020) · European Journal of Radiology · doi:10.1016/j.ejrad.2020.109478 · Cited by 14
Pordanjani, S. R., Hasanpour, A., Askarpour, H., Bastam, D., Rafiee, M., Khazaei, Z., Mazaheri, E., Vaziri, M. H., & Sabour, S. (2021) · International Journal of Preventive Medicine · doi:10.4103/ijpvm.ijpvm_469_20 · Cited by 14
Similarities and Differences of CT Features between COVID-19 Pneumonia and Heart Failure
Zhu, Z., Tang, J., Chai, X., Fang, Z., Liu, Q., Hu, X., Xu, D., He, J., Tang, L., Tai, S., et al. (2021) · Cardiovascular Innovations and Applications · doi:10.15212/cvia.2021.0016 · Cited by 14
Sharma, A. D., Kothari, N., Goel, A. D., Narayanan, B., Goyal, S., Bhatia, P. K., Kumar, D., Bohra, G. K., Chauhan, N. K., Jalandra, R., et al. (2021) · Journal of Family Medicine and Primary Care · doi:10.4103/jfmpc.jfmpc_14_21 · Cited by 14
Performance and Robustness of Machine Learning-based Radiomic COVID-19 Severity PredictionPreprint
Yip, S. S. F., Klaneček, Ž., Naganawa, S., Kim, J., Studen, A., Rivetti, L., & Jeraj, R. (2020) · medRxiv · doi:10.1101/2020.09.07.20189977 · Cited by 12
“Thus, the radiologist’s manual assessment of these CT-based features has demonstrated great promise in COVID-19 diagnosis [5, 8, 9], prognosis prediction [10], and determining disease severity and therapeutic response [6, 11, 12].”
BackgroundDetection of COVID-19 Disease from Chest X-Ray Images: A Deep Transfer Learning FrameworkPreprint
Sakib, S., Siddique, M. A. B., Khan, M. M. R., Yasmin, N., Aziz, A., Chowdhury, M., & Tasawar, I. K. (2020) · medRxiv · doi:10.1101/2020.11.08.20227819 · Cited by 16
“Furthermore, it is laborious, relatively expensive, and has a low sensitivity (60%–70%) [3].”
BackgroundDifficulties in False Negative Diagnosis of Coronavirus Disease 2019: A Case ReportPreprint
Hao, Q., Wu, H., & Wang, Q. (2020) · doi:10.21203/rs.3.rs-17319/v1 · Cited by 14
“7 phenomenon of false-negative results by the rRT-PCR assay have been reported in previous researches[7, 8], and it had also occurred in the diagnosis of Severe Acute Respiratory Syndrome (SARS) [9] and Middle East respiratory syndrome (MERS) [10].”
Conjunctival polymerase chain reaction-tests of 2019 novel coronavirus in patients in Shenyang,ChinaPreprint
Xu, L., Zhang, X., Song, W., Sun, B., Mu, J., Wang, B., Wang, Z., Cao, Y., & Dong, X. (2020) · doi:10.21203/rs.3.rs-17648/v1 · Cited by 14
Characteristics of Chest CT Images in Patients With COVID-19 Pneumonia in London, UK
Pakdemirli, E., Mandalia, U., & Monib, S. (2020) · Cureus · doi:10.7759/cureus.10289 · Cited by 13
Diagnostic approaches and potential therapeutic options for coronavirus disease 2019
Khan, Z., Ghafoor, D., Khan, A., Ualiyeva, D., Khan, S. A., Bilal, H., Khan, B. R., Khan, A., & Sajjad, W. (2020) · New Microbes and New Infections · doi:10.1016/j.nmni.2020.100770 · Cited by 13
“This study suggests that the presence of typical CT findings in people suspected of having the virus may be useful for initial screening [91].”
Challenges in COVID-19 diagnosis
Erenler, A. K., & Baydın, A. (2020) · Bratislavské lekárske listy/Bratislava medical journal · doi:10.4149/bll_2020_142 · Cited by 13
“Additionally, in the report, fi ve out of 167 patients, whose PCR tests were negative, were diagnosed as COVID-19 pneumonia according to chest CT fi ndings (83).”
Ju, Z., Ying, C., Ye, Z., Ma, D., Wang, B., & Cheng, Y. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.105970 · Cited by 13
Grubic, A. D., Ayazi, S., Zebarjadi, J., Tahmasbi, H., Ayazi, K., & Jobe, B. A. (2020) · World Journal of Gastrointestinal Surgery · doi:10.4240/wjgs.v12.i6.259 · Cited by 13
Xiao, Y., Shi, X., She, Q., Chen, Q., Pan, H., Jin, Z., Liu, X., Wu, H., Jin, W., Ke, G., et al. (2020) · Virulence · doi:10.1080/21505594.2020.1816076 · Cited by 13
“weakly positive in five patients with COVID-19 [6].”
BackgroundSelf-citationCT scan AI-aided triage for patients with COVID-19 in China
Vardhanabhuti, V. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30222-3 · Cited by 13
Huang, C., Zhou, S., Gao, L., Li, S., Cheng, Y., Zhou, W., Huang, Z., Ji, X., Fan, L., Zhang, F., et al. (2020) · Reproductive BioMedicine Online · doi:10.1016/j.rbmo.2020.11.015 · Cited by 13
“However, depending on the sample type, sample quality, RNA extraction method and RNA quality, the viral load may be lower than the detection limit of qRT-PCR, leading to false-negative results (Winichakoon et al., 2020; Xie et al., 2020).”
BackgroundDawoud, M. M., Dawoud, T. M., Ali, N. Y. A., & Nagy, H. A. (2020) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-020-00359-z · Cited by 13
“So, CT is more sensitive than RT-PCR and can decrease the chance of false-negative results in the RT-PCR assay [18, 19].”
BackgroundZhang, Y., Dai, C., Wang, H., Gao, Y., Li, T., Fang, Y., Shen, Z., Chen, L., Chen, Z., Ma, X., et al. (2020) · Virology Journal · doi:10.1186/s12985-020-01467-y · Cited by 13
“The clinical detection sensitivity of qRT-PCR is affected by various factors, such as 280 the nucleic acid extraction method, the one-step qRT-PCR reagent used, and the 281 primer/probe sets [23] .”
MethodBorghesi, A., Sverzellati, N., Polverosi, R., Balbi, M., Baratella, E., Busso, M., Calandriello, L., Cortese, G., Farchione, A., Iezzi, R., et al. (2021) · La radiologia medica · doi:10.1007/s11547-021-01385-1 · Cited by 13
Nair, A., McInnes, M. D. F., Jacob, B., Kumar, D., Soman, D. K., Valiyakath, H. S., Mahajan, P. S., Shah, M. A. H., Sabawi, M., & Heidous, M. A. (2021) · Emergency Radiology · doi:10.1007/s10140-021-01967-6 · Cited by 13
Perumal, V., Narayanan, V., & Rajasekar, S. J. S. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106336 · Cited by 13
Utilization of Transfer Learning Model in Detecting COVID-19 Cases From Chest X-Ray Images
Jawahar, M., Anbarasi, L. J., Prassanna, J., Manikandan, R., & Al-Turjman, F. M. (2021) · International Journal of E-Health and Medical Communications · doi:10.4018/ijehmc.20220701.oa2 · Cited by 13
“Due to the extreme shortage of the expensive test kit, Real-time polymerase chain reaction (RT-PCR) (Xie et al., 2020) has further aggravated the situation.”
BackgroundPulmonary vascular system: A vulnerable target for COVID‐19
Ai, J., Hong, W., Wu, M., & Wei, X. (2021) · MedComm · doi:10.1002/mco2.94 · Cited by 13
Comparison of HRCT Chest and RT-PCR in Diagnosis of COVID-19
Hanif, N., Rubi, G., Irshad, N., Ameer, S., Habib, U., & Zaidi, S. R. H. (2021) · Journal of College of Physicians And Surgeons Pakistan · doi:10.29271/jcpsp.2021.01.s1 · Cited by 13
Lima, T. M., Palamim, C. V. C., Melani, V. F., Mendes, M. F., Pereira, L. R., & Marson, F. A. L. (2022) · Diagnostics · doi:10.3390/diagnostics12061505 · Cited by 13
Liu, S., Li, G., Ding, L., Ding, J., Zhang, Q., Li, D., Hou, X., Kong, X., Zou, J., Zhang, S., et al. (2022) · Research · doi:10.34133/2022/9864089 · Cited by 12
Xu, H., Li, Y., Qiu, C. M., Jiao, B., Chen, Y., Tan, X., Chen, Z., Ai, L., Xiao, Y., Luo, A., et al. (2020) · medRxiv · doi:10.1101/2020.03.26.20043042 · Cited by 13
“As mentioned above, some patients presented characteristic radiographic features of COVID-19 from the first scan, while the nucleic acid test result was falsely negative (11) .”
BackgroundIslam, S. R., Prusty, D., & Manna, S. K. (2021) · medRxiv · doi:10.1101/2021.01.28.21250666 · Cited by 13
Detection of COVID-19 Cases In Chest X-ray Images Using Wavelets And Support Vector MachinesPreprint
Sarhan, A. M. (2020) · doi:10.21203/rs.3.rs-37558/v1 · Cited by 13
Update for Anaesthetists on Clinical Features of COVID-19 Patients and Relevant Management
Preckel, B., Schultz, M. J., Vlaar, A. P. J., Hulst, A. H., Hermanides, J., de Jong, M. D., Schlack, W. S., Stevens, M. F., Weenink, R. P., & Hollmann, M. W. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9051495 · Cited by 12
“Different scenarios have been described: chest CT may be negative for viral pneumonia in clinically symptomatic patients at initial presentation [30,31], but on the other hand patients who had negative RT-PCR for COVID-19 at initial presentation showed chest CT findings typical of viral pneumonia [32].”
Salamh, A. B. S., Salamah, A., & Akyüz, H. İ. (2021) · Radiology Research and Practice · doi:10.1155/2021/5554408 · Cited by 12
Potentials of post-mortem CT investigations during SARS-COV-2 pandemic: a narrative review
Filograna, L., Manenti, G., Ampanozi, G., Calcagni, A., Ryan, C. P., Floris, R., & Thali, M. J. (2022) · La radiologia medica · doi:10.1007/s11547-022-01457-w · Cited by 12
Negligible risk of surface transmission of SARS-CoV-2 in public transportation
Pilipenco, A., Forinová, M., Mašková, H., Hönig, V., Palus, M., Lynn, N. S., Jr., Víšová, I., Vrabcová, M., Houška, M., Anthi, J., et al. (2023) · Journal of Travel Medicine · doi:10.1093/jtm/taad065 · Cited by 12
Patil, S. V., Tandel, N., Kasture, L., & Gondhali, G. (2023) · Journal of Medicine · doi:10.3329/jom.v24i2.67268 · Cited by 12
A deep ensemble learning framework for COVID-19 detection in chest X-ray images
Asif, S., Ain, Q. U., Awais, M., Amjad, K., Bilal, O., Al-Sabri, R., & Abdullah, M. (2024) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-024-00466-1 · Cited by 12
“Researchers are working hard to overcome these limitations and improve the capability of RT-PCR (Xie et al. 2019).”
Rational and practical use of imaging in COVID-19 pneumonia
Sohail, S. (2020) · Pakistan Journal of Medical Sciences · doi:10.12669/pjms.36.covid19-s4.2760 · Cited by 12
Fang, X., Li, X., Bian, Y., Ji, X., & Lu, J. (2020) · European Radiology · doi:10.1007/s00330-020-06973-9 · Cited by 12
COVID-19 Ayurveda treatment protocol of governments of Nepal and India: a review and perspective
Pandit, R. D., & Singh, R. K. (2020) · Applied Science and Technology Annals · doi:10.3126/asta.v1i1.30276 · Cited by 13
O’Connell, D. A., Seikaly, H. R., Isaac, A., Pyne, J. M., Hart, R. D., Goldstein, D. P., & Yoo, J. J. (2020) · Journal of Otolaryngology - Head and Neck Surgery · doi:10.1186/s40463-020-00448-z · Cited by 12
A 21st Century Evil: Immunopathology and New Therapies of COVID-19
Silva, T. F., Tomiotto‐Pellissier, F., Sanfelice, R. A., Gonçalves, M. D., Bortoleti, B. T. D. S., Detoni, M. B., Rodrigues, A. C. J., Carloto, A. C. M., Concato, V. M., Siqueira, E. D. S., et al. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.562264 · Cited by 12
Misztal, K., Pocha, A., Durak-Kozica, M., Wątor, M., Kubica-Misztal, A., & Hartel, M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104092 · Cited by 12
Ravikanth, R. (2021) · Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging · doi:10.4103/ijri.ijri_377_20 · Cited by 12
Alamolhoda, S. Z., Zarghami, N., Kahroba, H., Mehdipour, A., Pourhassan‐Moghaddam, M., Jahanban‐Esfahlan, R., & Milani, M. (2021) · Advanced Pharmaceutical Bulletin · doi:10.34172/apb.2022.004 · Cited by 13
Herbal recommendations for treatment of COVID-19 symptoms according to Persian medicine
Kenari, H. M., Yousefsani, B. S., Eghbalian, F., Ghobadi, A., Jamshidi, A., & Mahroozade, S. (2021) · Journal of Medicinal Plants · doi:10.52547/jmp.20.77.1 · Cited by 12
Benbrahim, H., Hachimi, H., & Amine, A. (2021) · Walailak Journal of Science and Technology (WJST) · doi:10.48048/wjst.2021.13109 · Cited by 12
Radiographers and COVID-19 pneumonia: Diagnostic performance using CO-RADS
Vicini, S., Panvini, N., Bellini, D., Rengo, M., Ciotola, M., Vivo, M. D., Gambaretto, C., Caldon, V., Panno, S., Borgo, C. D., et al. (2021) · Radiography · doi:10.1016/j.radi.2021.04.010 · Cited by 12
Samir, A., El-Husseiny, R. M., Sweed, R. A., El-Maaboud, N. A. E.-M. A., & Masoud, M. (2021) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-021-00512-2 · Cited by 12
“However, in mild cases with low viral load, many patients had false-negative swab tests [2].”
BackgroundScreening of COVID-19 Patients Using Deep Learning and IoT Framework
Kaushik, H., Singh, D., Tiwari, S., Kaur, M., Jeong, C., Nam, Y., & Khan, M. A. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2021.017337 · Cited by 12
Wang, D., Wang, Z., Gao, Y., Wu, X. J., Dong, L., Dai, X., & Gao, Y. (2021) · Journal of Virological Methods · doi:10.1016/j.jviromet.2021.114285 · Cited by 12
A comprehensive review on efficient approaches for combating coronaviruses
Pouresmaieli, M., Ekrami, E., Akbari, A., Noorbakhsh, N., Moghadam, N. B., & Mamoudifard, M. (2021) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2021.112353 · Cited by 12
“In rRT-PCR, a false negative rate of up to 20% has been reported [63,69].”
COVID-19 Pneumonia Classification Based on NeuroWavelet Capsule Network
Monday, H. N., Li, J., Nneji, G. U., Nahar, S., Hossin, M. A., & Jackson, J. K. (2022) · Healthcare · doi:10.3390/healthcare10030422 · Cited by 13
A COVID‐19 CXR image recognition method based on MSA‐DDCovidNet
Wang, W., Huang, W., Wang, X., Zhang, P., & Zhang, N. (2022) · IET Image Processing · doi:10.1049/ipr2.12474 · Cited by 12
“It has high specificity, but the current demand for detection kits is increasing [1].”
BackgroundAlmasi, A., Shahnazari, R., Abbasi, M. A., Divsalar, F., Bayazidi, M., & Sadatnaseri, A. (2022) · The Egyptian Journal of Radiology and Nuclear Medicine · doi:10.1186/s43055-022-00781-5 · Cited by 12
FCF: Feature complement fusion network for detecting COVID-19 through CT scan images
Liang, S. Y., Nie, R., Cao, J., Wang, X., & Zhang, G. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109111 · Cited by 11
“Recent theoretical developments have revealed that the nearest clinical diagnosis regards the real-time reverse transcriptase-polymerase chain reaction (RT-PCR) as the golden standard to diagnose COVID-19 [7].”
Aksoy, B., & SALMAN, O. K. M. (2022) · Türk doğa ve fen dergisi :/Türk doğa ve fen dergisi · doi:10.46810/tdfd.1095624 · Cited by 14
Dasgupta, A., Bakshi, A., Mukherjee, S., Das, K., Talukdar, S., Chatterjee, P., Mondal, S., Das, P., Ghosh, S., Som, A., et al. (2022) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1462 · Cited by 10
“It shows that the use of chest CT can be significant to decrease false-negative data (Xie et al., 2020) during the early phase of COVID-19.”
BackgroundMethodSVM on Top of Deep Networks for Covid-19 Detection from Chest X-ray Images
Do, T., Le, V.-T., & Doan, T.-H. (2022) · Journal of information and communication convergence engineering · doi:10.56977/jicce.2022.20.3.219 · Cited by 13
Using a 3D ResNet for Detecting the Presence and Severity of COVID-19 from CT Scans
Turnbull, R. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-25082-8_45 · Cited by 12
Diagnosis of COVID-19: Considerations, Controversies and Challenges in South Africa
Dheda, K. U. J., Davids, M., Chang, J.-W., Gina, P., Pooran, A., Makambwa, E., Esmail, A., Vardas, E., & Preiser, W. (2020) · Wits Journal of Clinical Medicine · doi:10.18772/26180197.2020.v2nsia1 · Cited by 12
“(20) It has since been shown that specimens from the lower respiratory tract have a higher viral load and hence more likely to test positive than specimens from the upper respiratory tract.”
Background
Metadata
- Authors
- Xie, X., Zhong, Z., Zhao, W., Zheng, C., Wang, F., & Liu, J.
- Affiliations
- Central South University (CN)
- Corresponding author
- Liu, Jun
- Venue
- Radiology
- Published
- 2020-08
- Volume
- 296
- Issue
- 2
- Pages
- E41-E45
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC7233363
- OpenAlex
- W3006110666
- Cited by
- 1,960
- References
- 6
- Citations by year
- 2026: 16 · 2025: 24 · 2024: 75 · 2023: 136 · 2022: 282 · 2021: 575 · 2020: 850
- FWCI
- 60.46
- 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 detection and testing
- Keywords
- COVID-19 pandemic, COVID-19 pneumonia, chest CT findings, ground-glass opacities, delayed diagnosis
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, COVID-19 Vaccines, COVID-19 Testing, Adult, Aged, False Negative Reactions, Female, Humans, Male, Middle Aged, Pneumonia, Viral, Radiographic Image Interpretation, Computer-Assisted, Tomography, X-Ray Computed, Reproducibility of Results, Coronavirus Infections, Clinical Laboratory Techniques, Reverse Transcriptase Polymerase Chain Reaction, Pandemics
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