Reliability of Machine Learning in Eliminating Data Redundancy of Radiomics and Reflecting Pathophysiology in COVID-19 Pneumonia: Impact of CT Reconstruction Kernels on Accuracy

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...

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Published in:	IEEE access 2022, Vol.10, p.120901-120921
Main Authors:	Statsenko, Yauhen, Habuza, Tetiana, Talako, Tatsiana, Kurbatova, Tetiana, Simiyu, Gillian Lylian, Smetanina, Darya, Sido, Juana, Qandil, Dana Sharif, Meribout, Sarah, Gelovani, Juri G., Gorkom, Klaus Neidl-Van, Almansoori, Taleb M., Zahmi, Fatmah Al, Loney, Tom, Bedson, Anthony, Naidoo, Nerissa, Dehdashtian, Alireza, Ljubisavljevic, Milos, Koteesh, Jamal Al, Das, Karuna M.
Format:	Article
Language:	English
Subjects:	Blended machine learning model Computed tomography COVID-19 functional outcomes lung structural changes Lungs Machine learning Pneumonia Predictive models Pulmonary diseases Radiomics Redundancy SARC-CoV-2 structure-function association
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