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Rapid quantification of COVID-19 pneumonia burden from computed tomography with convolutional LSTM networks.
Grodecki K, Killekar A, Lin A, Cadet S, McElhinney P, Razipour A, Chan C, Pressman BD, Julien P, Simon J, Maurovich-Horvat P, Gaibazzi N, Thakur U, Mancini E, Agalbato C, Munechika J, Matsumoto H, Menè R, Parati G, Cernigliaro F, Nerlekar N, Torlasco C, Pontone G, Dey D, Slomka PJ. Grodecki K, et al. Among authors: lin a. ArXiv [Preprint]. 2021 Mar 31:arXiv:2104.00138v3. ArXiv. 2021. Update in: J Med Imaging (Bellingham). 2022 Sep;9(5):054001. doi: 10.1117/1.JMI.9.5.054001. PMID: 33821209 Free PMC article. Updated. Preprint.
Artificial intelligence-assisted quantification of COVID-19 pneumonia burden from computed tomography improves prediction of adverse outcomes over visual scoring systems.
Grodecki K, Killekar A, Simon J, Lin A, Cadet S, McElhinney P, Chan C, Williams MC, Pressman BD, Julien P, Li D, Chen P, Gaibazzi N, Thakur U, Mancini E, Agalbato C, Munechika J, Matsumoto H, Menè R, Parati G, Cernigliaro F, Nerlekar N, Torlasco C, Pontone G, Maurovich-Horvat P, Slomka PJ, Dey D. Grodecki K, et al. Among authors: lin a. Br J Radiol. 2023 Sep;96(1149):20220180. doi: 10.1259/bjr.20220180. Epub 2023 Jun 27. Br J Radiol. 2023. PMID: 37310152 Free article.
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[No authors listed] [No authors listed] PMID: 38245285
5,740 results