The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR
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In a series of 51 patients with chest CT and real-time polymerase chain reaction assay (RT-PCR) performed within 3 days, the sensitivity of CT for 2019 novel coronavirus infection was 98% and that ...
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CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV)
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“Recently, Xie reported 5/167 (3%) patients who had negative RT-PCR for COVID-19 at initial presentation despite chest CT findings typical of viral pneumonia [3].”
Background
Cited by
Coronavirus Disease 2019 (COVID-19): A Perspective from China
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Explainable Deep Learning for Pulmonary Disease and Coronavirus COVID-19 Detection from X-rays
Brunese, L., Mercaldo, F., Reginelli, A., & Santone, A. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105608 · Cited by 623
Harmon, S. A., Sanford, T. H., Xu, S., Turkbey, E. B., Roth, H., Xu, Z., Yang, D., Myronenko, A., Anderson, V., Amalou, A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17971-2 · Cited by 587
Coronavirus disease 2019 (COVID-19) CT findings: A systematic review and meta-analysis
Bao, C., Liu, X., Zhang, H., Li, Y., & Liu, J. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.03.006 · Cited by 446
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Shan, B., Broza, Y. Y., Li, W., Wang, Y., Wu, S., Liu, Z., Wang, J., Gui, S., Wang, L., Zhang, Z., et al. (2020) · ACS Nano · doi:10.1021/acsnano.0c05657 · Cited by 384
Hernández-Fernández, F., Sandoval Valencia, H., Barbella-Aponte, R. A., Collado-Jiménez, R., Ayo-Martín, Ó., Barrena, C., Molina-Nuevo, J. D., García-García, J., Lozano-Setién, E., Alcahut-Rodriguez, C., et al. (2020) · Brain · doi:10.1093/brain/awaa239 · Cited by 280
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Mollura, D. J., Culp, M. P., Pollack, E., Battino, G., Scheel, J. R., Mango, V. L., Elahi, A., Schweitzer, A., & Dako, F. (2020) · Radiology · doi:10.1148/radiol.2020201434 · Cited by 228
Ojha, V., Mani, A., Pandey, N. N., Sharma, S., & Kumar, S. (2020) · European Radiology · doi:10.1007/s00330-020-06975-7 · Cited by 227
Larson, D. B., Harvey, H., Rubin, D. L., Irani, N., Tse, J. R., & Langlotz, C. P. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.09.060 · Cited by 198
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168
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Liu, F., Zhu, T., Wu, X., Yang, B., You, C., Wang, C., Lu, L., Liu, Z., Zheng, Y., Sun, X., et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00952-2 · Cited by 155
Adly, A. S., Adly, A. S., & Adly, M. S. (2020) · Journal of Medical Internet Research · doi:10.2196/19104 · Cited by 138
Kissler, S. M., Kishore, N., Prabhu, M., Goffman, D., Beilin, Y., Landau, R., Gyamfi-Bannerman, C., Bateman, B. T., Snyder, J., Razavi, A. S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18271-5 · Cited by 129
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Yan, Q., Wang, B., Gong, D., Luo, C., Zhao, W., Shen, J., Shi, Q., Jin, S., Zhang, L., & You, Z. (2020) · arXiv · doi:10.48550/arxiv.2004.10987 · Cited by 109
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Bai, X., Wang, H., Ma, L., Xu, Y., Gan, J., Fan, Z., Yang, F., Ma, K., Yang, J., Bai, S., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00421-z · Cited by 75
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Bayoudh, K., Hamdaoui, F., & Mtibaa, A. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00957-1 · Cited by 50
Inui, S., Gonoi, W., Kurokawa, R., Nakai, Y., Watanabe, Y., Sakurai, K., Ishida, M., Fujikawa, A., & Abe, O. (2021) · Insights into Imaging · doi:10.1186/s13244-021-01096-1 · Cited by 42
Kim, Y., Park, J. Y., Hwang, E. J., Lee, S. M., & Park, C. M. (2021) · Journal of Thoracic Disease · doi:10.21037/jtd-21-1342 · Cited by 31
Park, J., Kwak, I.-Y., & Lim, C. (2021) · Electronics · doi:10.3390/electronics10161996 · Cited by 24
Özer, H., Kılınçer, A., Uysal, E., Yormaz, B., Cebeci, H., Durmaz, M. S., & Koplay, M. (2021) · Japanese Journal of Radiology · doi:10.1007/s11604-021-01128-2 · Cited by 7
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Metadata
- Authors
- Fang, Y., Zhang, H., Xie, J., Lin, M., Ying, L., Pang, P., & Ji, W.
- Venue
- Radiology
- Published
- 2020-08
- Volume
- 296
- Issue
- 2
- Pages
- E115-E117
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- OpenAlex
- W3008985036
- Cited by
- 3,354
- References
- 3
- FWCI
- 95.00
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
