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For: Mergen V, Kobe A, Blüthgen C, Euler A, Flohr T, Frauenfelder T, Alkadhi H, Eberhard M. Deep learning for automatic quantification of lung abnormalities in COVID-19 patients: First experience and correlation with clinical parameters. Eur J Radiol Open 2020;7:100272. [PMID: 33043101 DOI: 10.1016/j.ejro.2020.100272] [Cited by in Crossref: 4] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021;11:1317. [PMID: 34441252 DOI: 10.3390/diagnostics11081317] [Reference Citation Analysis]
2 Okuma T, Hamamoto S, Maebayashi T, Taniguchi A, Hirakawa K, Matsushita S, Matsushita K, Murata K, Manabe T, Miki Y. Quantitative evaluation of COVID-19 pneumonia severity by CT pneumonia analysis algorithm using deep learning technology and blood test results. Jpn J Radiol 2021. [PMID: 33988788 DOI: 10.1007/s11604-021-01134-4] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
3 Qanadli SD, Sauter AW, Alkadhi H, Christe A, Poletti PA, Ebner L, Rotzinger DC. Vascular Abnormalities Detected with Chest CT in COVID-19: Spectrum, Association with Parenchymal Lesions, Cardiac Changes, and Correlation with Clinical Severity (COVID-CAVA Study). Diagnostics (Basel) 2021;11:606. [PMID: 33805443 DOI: 10.3390/diagnostics11040606] [Reference Citation Analysis]