A comparison of Covid-19 early detection between convolutional neural networks and radiologists

• Published in: Insights into imaging Vol. 13; no. 1; p. 122
• Main Authors: Albiol, Alberto, Albiol, Francisco, Paredes, Roberto, Plasencia-Martínez, Juana María, Blanco Barrio, Ana, Santos, José M. García, Tortajada, Salvador, González Montaño, Victoria M., Rodríguez Godoy, Clara E., Fernández Gómez, Saray, Oliver-Garcia, Elena, de la Iglesia Vayá, María, Márquez Pérez, Francisca L., Rayo Madrid, Juan I.
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 28-07-2022; Springer Nature B.V; SpringerOpen
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Chest; Coronaviruses; COVID-19; Datasets; Deep learning; Diagnostic Radiology; Imaging; Internal Medicine; Interventional Radiology; Machine learning; Medical imaging; Medicine; Medicine & Public Health; Neuroradiology; Original; Original Article; Radiographs; Radiology; Ultrasound; Deep learning; Covid-19; Radiology
• ISSN: 1869-4101; 1869-4101
• DOI: 10.1186/s13244-022-01250-3

Background
The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience.
Methods
The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx.
Results
Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx.
Conclusion
The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.