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

Autor: Albiol A, Albiol F, Paredes R, Plasencia-Martínez JM, Blanco Barrio A, Santos JMG, Tortajada S, González Montaño VM, Rodríguez Godoy CE, Fernández Gómez S, Oliver-Garcia E, de la Iglesia Vayá M, Márquez Pérez FL, Rayo Madrid JI
Rok vydání: 2022
Předmět:
Zdroj: Insights into Imaging
r-CIPF. Repositorio Institucional Producción Científica del Centro de Investigación Principe Felipe (CIPF)
Universitat Rovira i virgili (URV)
r-FISABIO. Repositorio Institucional de Producción Científica
instname
ISSN: 1869-4101
Popis: 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.
Databáze: OpenAIRE
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