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

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 o...

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Detalles Bibliográficos
Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Vienna 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9330942/
https://www.ncbi.nlm.nih.gov/pubmed/35900673
http://dx.doi.org/10.1186/s13244-022-01250-3
Descripción
Sumario: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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13244-022-01250-3.