Statsenko Y.Habuza T.Talako T.Kurbatova T.Simiyu G.L.Smetanina D.Sido J.Qandil D.S.Meribout S.Gelovani J.G.Gorkom K.N.V.Almansoori T.M.Zahmi F.A.Loney T.Bedson A.Naidoo N.Dehdashtian A.Ljubisavljevic M.Koteesh J.A.Das K.M.Mahidol University2023-06-182023-06-182022-01-01IEEE Access Vol.10 (2022) , 120901-120921https://repository.li.mahidol.ac.th/handle/123456789/84356Background: Radiomical data are redundant but they might serve as a tool for lung quantitative assessment reflecting disease severity and actual physiological status of COVID-19 patients. Objective: Test the effectiveness of machine learning in eliminating data redundancy of radiomics and reflecting pathophysiologic changes in patients with COVID-19 pneumonia. Methods: We analyzed 605 cases admitted to Al Ain Hospital from 24 February to 1 July, 2020. They met the following inclusion criteria: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We categorized cases into 4 classes: mild <5% of pulmonary parenchymal involvement, moderate - 5-24%, severe - 25-49%, and critical ≥50 %. We used CT scans to build regression models predicting the oxygenation level, respiratory and cardiovascular functioning. Results: Radiomical findings are a reliable source of information to assess the functional status of patients with COVID-19. Machine learning models can predict the oxygenation level, respiratory andComputer ScienceArticleSCOPUS10.1109/ACCESS.2022.32110802-s2.0-8513944478921693536