The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach
Open accessAbstract
Purpose To quantitatively evaluate lung burden changes in patients with coronavirus disease 2019 (COVID-19) by using serial CT scan by an automated deep learning method. Materials and Methods Patients with COVID-19, who underwent chest CT between January 1 and February 3, 2020, were retrospectively evaluated. The patients were divided into mild, moderate, severe, and critical types, according to their baseline clinical, laboratory, and CT findings. CT lung opacification percentages of the whole lung and five lobes were automatically quantified by a commercial deep learning software and compared with those at follow-up CT scans. Longitudinal changes of the CT quantitative parameter were also compared among the four clinical types. Results A total of 126 patients with COVID-19 (mean age, 52 years ± 15 [standard deviation]; 53.2% males) were evaluated, including six mild, 94 moderate, 20 severe, and six critical cases. CT-derived opacification percentage was significantly different among clinical groups at baseline, gradually progressing from mild to critical type (all P < .01). Overall, the whole-lung opacification percentage significantly increased from baseline CT to first follow-up CT (median [interquartile range]: 3.6% [0.5%, 12.1%] vs 8.7% [2.7%, 21.2%]; P < .01). No significant progression of the opacification percentages was noted from the first follow-up to second follow-up CT (8.7% [2.7%, 21.2%] vs 6.0% [1.9%, 24.3%]; P = .655). Conclusion The quantification of lung opacification in COVID-19 measured at chest CT by using a commercially available deep learning–based tool was significantly different among groups with different clinical severity. This approach could potentially eliminate the subjectivity in the initial assessment and follow-up of pulmonary findings in COVID-19. Supplemental material is available for this article. Keywords: Adults and Pediatrics, CT-Quantitative, Infection, Inflammation, Lung, Pulmonary © RSNA, 2020
Overview
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
Cites
Statistics
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
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
Pathological findings of COVID-19 associated with acute respiratory distress syndrome
Xu, Z., Shi, L., Wang, Y., Zhang, J., Huang, L., Zhang, C., Liu, S., Zhao, P., Liu, H., Zhu, L., et al. (2020) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(20)30076-x · Cited by 8,545
Self-citationMacMahon, H., Naidich, D. P., Goo, J. M., Lee, K. S., Leung, A. N. C., Mayo, J. R., Mehta, A. C., Ohno, Y., Powell, C. A., Prokop, M., et al. (2017) · Radiology · doi:10.1148/radiol.2017161659 · Cited by 2,491
Cited by
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2987975 · Cited by 1,404
A Noise-Robust Framework for Automatic Segmentation of COVID-19 Pneumonia Lesions From CT Images
Wang, G., Liu, X., Li, C., Xu, Z., Ruan, J., Zhu, H., Meng, T., Li, K., Huang, N., & Zhang, S. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.3000314 · Cited by 469
Well-aerated Lung on Admitting Chest CT to Predict Adverse Outcome in COVID-19 Pneumonia
Colombi, D., Bodini, F. C., Petrini, M., Maffi, G., Morelli, N., Milanese, G., Silva, M., Sverzellati, N., & Michieletti, E. (2020) · Radiology · doi:10.1148/radiol.2020201433 · Cited by 456
Yang, D., Xu, Z., Li, W., Myronenko, A., Roth, H. R., Harmon, S., Xu, S., Turkbey, B., Turkbey, E., Wang, X., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101992 · Cited by 279
Applications of artificial intelligence in battling against covid-19: A literature review
Tayarani N., M.-H. (2021) · Chaos, Solitons & Fractals · doi:10.1016/j.chaos.2020.110338 · Cited by 210
Wehbe, R. M., Sheng, J., Dutta, S., Chai, S., Dravid, A., Barutcu, S., Wu, Y., Cantrell, D. R., Xiao, N., Allen, B. D., et al. (2021) · Radiology · doi:10.1148/radiol.2020203511 · Cited by 190
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
Metadata
- Authors
- Huang, L., Han, R., Ai, T., Yu, P., Kang, H., Tao, Q., & Xia, L.
- Venue
- Radiology: Cardiothoracic Imaging
- Published
- 2020-04-01
- Volume
- 2
- Issue
- 2
- Pages
- e200075
- ISSN
- 2638-6135 (ISSN-L)
- OpenAlex
- W3013633552
- Cited by
- 431
- References
- 4
- FWCI
- 43.93
- Type
- journal article
- Language
- en
- Open access
- green
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
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
