Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.

Journal: Iranian journal of medical sciences

PMID: 34840382

Abstract

BACKGROUND: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task.

Authors

Mohsen Tabatabaei

Health Information Management, Office of Vice Chancellor for Research, Arak University of Medical Sciences, Arak, Iran.
Baharak Tasorian

Internal Medicine Department, Arak University of Medical Sciences, Arak, Iran.
Manu Goyal

Department of Computing and Mathematics, Manchester Metropolitan University, UK.
Abdollatif Moini

Department of Internal Medicine of Amir Al Momenin Hospital, Arak University of Medical Sciences, Arak, Iran.
Houman Sotoudeh

Department of Radiology, University of Alabama at Birmingham (UAB), Birmingham, Alabama, USA.

Keywords

Artificial Intelligence COVID-19 Diagnosis, Differential Feasibility Studies Female Humans Influenza A Virus, H1N1 Subtype Influenza, Human Male Pneumonia, Viral Retrospective Studies Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (34840382)

Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.

Abstract

Authors

Keywords

External Resources

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