A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.

Autor: Ardakani AA; Radiology Technology Department, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Kwee RM; Department of Radiology, Zuyderland Medical Center, Heerlen/Sittard-Geleen, the Netherlands., Mirza-Aghazadeh-Attari M; Medical Radiation Sciences Research Group, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran., Castro HM; Pulmonology Department, Hospital Italiano de Buenos Aires, Argentina., Kuzan TY; Department of Radiology, Sancaktepe Sehit Prof. Dr. Ilhan Varank Training and Research Hospital, Istanbul, Turkey., Altintoprak KM; Department of Radiology, Sancaktepe Sehit Prof. Dr. Ilhan Varank Training and Research Hospital, Istanbul, Turkey., Besutti G; Radiology Department, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy.; Clinical and Experimental Medicine PhD program, University of Modena and Reggio Emilia, Modena, Italy., Monelli F; Radiology Department, Azienda USL - IRCCS di Reggio Emilia, Reggio Emilia, Italy.; Clinical and Experimental Medicine PhD program, University of Modena and Reggio Emilia, Modena, Italy., Faeghi F; Radiology Technology Department, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran., Acharya UR; Ngee Ann Polytechnic, Department of Electronics and Computer Engineering, 599489, Singapore.; Department of Biomedical Engineering, School of Science and Technology, SUSS University, Singapore.; Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan., Mohammadi A; Department of Radiology, Faculty of Medicine, Urmia University of Medical Science, Urmia, Iran.
Jazyk: angličtina
Zdroj: Pattern recognition letters [Pattern Recognit Lett] 2021 Dec; Vol. 152, pp. 42-49. Date of Electronic Publication: 2021 Sep 23.
DOI: 10.1016/j.patrec.2021.09.012
Abstrakt: Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.
Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(© 2021 Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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