Development of computer-aided model to differentiate COVID-19 from pulmonary edema in lung CT scan: EDECOVID-net.

Autor: Velichko E; Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia., Shariaty F; Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia. Electronic address: shariaty3@gmail.com., Orooji M; Department of Electrical and Computer Engineering, University of California, Davis, United States., Pavlov V; Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia., Pervunina T; Institute of Perinatology and Pediatrics, Almazov National Medical Research Centre, St. Petersburg, Russia., Zavjalov S; Institute of Electronics and Telecommunications, Peter the Great St.Petersburg Polytechnic University, Saint-Petersburg, Russia., Khazaei R; Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran., Radmard AR; Department of Radiology, Shariati Hospital, Tehran University of Medical Sciences, Tehran, Iran.
Jazyk: angličtina
Zdroj: Computers in biology and medicine [Comput Biol Med] 2022 Feb; Vol. 141, pp. 105172. Date of Electronic Publication: 2021 Dec 28.
DOI: 10.1016/j.compbiomed.2021.105172
Abstrakt: The efforts made to prevent the spread of COVID-19 face specific challenges in diagnosing COVID-19 patients and differentiating them from patients with pulmonary edema. Although systemically administered pulmonary vasodilators and acetazolamide are of great benefit for treating pulmonary edema, they should not be used to treat COVID-19 as they carry the risk of several adverse consequences, including worsening the matching of ventilation and perfusion, impaired carbon dioxide transport, systemic hypotension, and increased work of breathing. This study proposes a machine learning-based method (EDECOVID-net) that automatically differentiates the COVID-19 symptoms from pulmonary edema in lung CT scans using radiomic features. To the best of our knowledge, EDECOVID-net is the first method to differentiate COVID-19 from pulmonary edema and a helpful tool for diagnosing COVID-19 at early stages. The EDECOVID-net has been proposed as a new machine learning-based method with some advantages, such as having simple structure and few mathematical calculations. In total, 13 717 imaging patches, including 5759 COVID-19 and 7958 edema images, were extracted using a CT incision by a specialist radiologist. The EDECOVID-net can distinguish the patients with COVID-19 from those with pulmonary edema with an accuracy of 0.98. In addition, the accuracy of the EDECOVID-net algorithm is compared with other machine learning methods, such as VGG-16 (Acc = 0.94), VGG-19 (Acc = 0.96), Xception (Acc = 0.95), ResNet101 (Acc = 0.97), and DenseNet20l (Acc = 0.97).
(Copyright © 2022 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: