A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study
Article
Ardakani, Ali Abbasian, Kwee, Robert M., Mirza-Aghazadeh-Attari, Mohammad, Castro, Horacio Matias, Kuzan, Taha Yusuf, Altintoprak, Kubra Murzoglu, Besutti, Giulia, Monelli, Filippo, Faeghi, Fairborz, Acharya, U Rajendra and Mohammadi, Afshin. 2021. "A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study." Pattern Recognition Letters. 152, pp. 42-49. https://doi.org/10.1016/j.patrec.2021.09.012
Article Title | A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study |
---|---|
ERA Journal ID | 18106 |
Article Category | Article |
Authors | Ardakani, Ali Abbasian, Kwee, Robert M., Mirza-Aghazadeh-Attari, Mohammad, Castro, Horacio Matias, Kuzan, Taha Yusuf, Altintoprak, Kubra Murzoglu, Besutti, Giulia, Monelli, Filippo, Faeghi, Fairborz, Acharya, U Rajendra and Mohammadi, Afshin |
Journal Title | Pattern Recognition Letters |
Journal Citation | 152, pp. 42-49 |
Number of Pages | 8 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0167-8655 |
1872-7344 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.patrec.2021.09.012 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0167865521003391 |
Abstract | 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. © 2021 |
Keywords | Artificial intelligence |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Shahid Beheshti University of Medical Sciences, Iran |
Zuyderland Medical Centre, Netherlands | |
Tabriz University of Medical Sciences, Iran | |
Hospital Italiano de Buenos Aires, Argentina | |
Sancaktepe Sehit Prof. Dr. Ilhan Varank Training and Research Hospital, Turkey | |
Local Health Authority - IRCCS of Reggio Emilia, Italy | |
University of Modena and Reggio Emilia, Italy | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan | |
Urmia University of Medical Science, Iran |
Permalink -
https://research.usq.edu.au/item/z1v55/a-practical-artificial-intelligence-system-to-diagnose-covid-19-using-computed-tomography-a-multinational-external-validation-study
19
total views0
total downloads8
views this month0
downloads this month