{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["36(2)"],"submitter":["Di Napoli A"],"funding":["Università degli Studi di Roma La Sapienza"],"pubmed_abstract":["Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external valida"],"journal":["Journal of digital imaging"],"pagination":["603-616"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9713092"],"repository":["biostudies-literature"],"pubmed_title":["3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients."],"pmcid":["PMC9713092"],"pubmed_authors":["Pasquini L","Napolitano A","Ortis P","Tagliente E","Capotondi C","Boellis A","Bernardini A","Ranieri SC","Franchi P","Pietrantonio F","Voicu IP","Cipriano E","Curti S","Stefanini T","Di Napoli A","Angeletti C"],"additional_accession":[]},"is_claimable":false,"name":"3D CT-Inclusive Deep-Learning Model to Predict Mortality, ICU Admittance, and Intubation in COVID-19 Patients.","description":"Chest CT is a useful initial exam in patients with coronavirus disease 2019 (COVID-19) for assessing lung damage. AI-powered predictive models could be useful to better allocate resources in the midst of the pandemic. Our aim was to build a deep-learning (DL) model for COVID-19 outcome prediction inclusive of 3D chest CT images acquired at hospital admission. This retrospective multicentric study included 1051 patients (mean age 69, SD = 15) who presented to the emergency department of three different institutions between 20th March 2020 and 20th January 2021 with COVID-19 confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR). Chest CT at hospital admission were evaluated by a 3D residual neural network algorithm. Training, internal validation, and external valida","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Apr","modification":"2025-04-18T21:52:57.643Z","creation":"2025-02-19T01:57:05.781Z"},"accession":"S-EPMC9713092","cross_references":{"pubmed":["36450922"],"doi":["10.1007/s10278-022-00734-4"]}}