TY - JOUR
T1 - Reliability of Machine Learning in Eliminating Data Redundancy of Radiomics and Reflecting Pathophysiology in COVID-19 Pneumonia
T2 - Impact of CT Reconstruction Kernels on Accuracy
AU - Statsenko, Yauhen
AU - Habuza, Tetiana
AU - Talako, Tatsiana
AU - Kurbatova, Tetiana
AU - Simiyu, Gillian Lylian
AU - Smetanina, Darya
AU - Sido, Juana
AU - Qandil, Dana Sharif
AU - Meribout, Sarah
AU - Gelovani, Juri G.
AU - Neidl, Klaus Friedrich
AU - Almansoori, Taleb M.
AU - Zahmi, Fatmah Al
AU - Loney, Tom
AU - Bedson, Anthony
AU - Naidoo, Nerissa
AU - Dehdashtian, Alireza
AU - Ljubisavljevic, Milos
AU - Koteesh, Jamal Al
AU - Das, Karuna M.
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2022
Y1 - 2022
N2 - Background: 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.
AB - Background: 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.
KW - Blended machine learning model
KW - COVID-19
KW - SARC-CoV-2
KW - functional outcomes
KW - lung structural changes
KW - pneumonia
KW - radiomics
KW - structure-function association
UR - https://www.scopus.com/pages/publications/85139444789
UR - https://www.scopus.com/pages/publications/85139444789#tab=citedBy
U2 - 10.1109/ACCESS.2022.3211080
DO - 10.1109/ACCESS.2022.3211080
M3 - Article
AN - SCOPUS:85139444789
SN - 2169-3536
VL - 10
SP - 120901
EP - 120921
JO - IEEE Access
JF - IEEE Access
ER -