Author: Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Alienor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Teodor Grand; Jules Gregory; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stephane Tran Ba; Valerie Bousson; Marie-Pierre Revel; Nikos Paragios
Title: AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia Document date: 2020_4_22
ID: nxm1jr0x_11
Snippet: This retrospective multi-center study was approved by our Institutional Review Board (AAA-2020-08007) which waived the need for patients' consent. Patients diagnosed with COVID-19 from March 4th to 29th at six large University Hospitals were eligible if they had positive PCR-RT and signs of COVID-19 pneumonia on unenhanced chest CT. A total of 478 patients formed the full dataset (208, 668 CT slices). Only one CT examination was included for each.....
Document: This retrospective multi-center study was approved by our Institutional Review Board (AAA-2020-08007) which waived the need for patients' consent. Patients diagnosed with COVID-19 from March 4th to 29th at six large University Hospitals were eligible if they had positive PCR-RT and signs of COVID-19 pneumonia on unenhanced chest CT. A total of 478 patients formed the full dataset (208, 668 CT slices). Only one CT examination was included for each patient. Exclusion criteria were (i) contrast medium injection and (ii) important motion artifacts.
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