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AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

Paolo Soda, Natascha Claudia D’Amico, Jacopo Tessadori, Giovanni Valbusa, Valerio Guarrasi, Chandra Bortolotto, Muhammad Usman Akbar, Rosa Sicilia, Ermanno Cordelli, Deborah Fazzini, Michaela Cellina, Giancarlo Oliva, Giovanni Callea, Silvia Panella, Maurizio Cariati, Diletta Cozzi, Vittorio Miele, Elvira Stellato, Gianpaolo Carrafiello, Giulia Castorani, Annalisa Simeone, Lorenzo Preda, Giulio Iannello, Alessio Del Bue, Fabio Tedoldi, Marco Alì, Diego Sona, Sergio Papa

2021Florence Research (University of Florence)91 citationsDOIOpen Access PDF

Topics & Concepts

Radiological weaponMedicineCoronavirus disease 2019 (COVID-19)EpidemiologyIdentification (biology)Intensive careEconomic shortageTriageMedical emergencyEmergency medicineMedical physicsArtificial intelligenceIntensive care medicineDiseaseRadiologyComputer scienceInternal medicineInfectious disease (medical specialty)BiologyLinguisticsPhilosophyGovernment (linguistics)BotanyCOVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education
AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study | Litcius