XRay4COVID
XRay4COVID (hereinafter referred to as TOOL) has been developed to give aid in the crisis situation resulting from the coronavirus pandemic. It is not a certified product so it should be noted that the user assumes all responsibility and risk for the use of the TOOL. Under no circumstances shall developers, employing institutions or any other institution or person involved in the development or maintenance of the TOOL be liable for any direct or indirect, intentional or unintentional damages or lost profits resulting from the use of the TOOL or any subsequent use of the results thereof.
Covid-19 is a new infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Given the seriousness of the situation, the World Health Organization declared a global pandemic as the Covid-19 rapidly around the world. Among its applications, chest X-ray images are frequently used for an early diagnostic/screening of Covid-19 disease, given the frequent pulmonary impact on the patients, critical issue to prevent further complications caused by this highly infectious disease.
XRay4COVID provides a framework for clinical specialists or other experts in the field to work collaboratively on chest X-ray images using a fast, reliable and distributed application. XRay4COVID uses techniques of Artificial Intelligence to estimate the risk of pathology, and, in particular, the risk of Covid-19 from X-ray images. Different preprints and published works of the used methods are available at:
- J. de Moura, J. Novo, M. Ortega, "Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images", preprint from medRxiv, 2020. DOI: https://doi.org/10.1101/2020.05.01.20087254
- J. de Moura, L. Ramos, P. L. Vidal, M. Cruz, L. Abelairas, E. Castro, J. Novo, M. Ortega, "Deep Convolutional Approaches for the Analysis of COVID-19 Using Chest X-Ray Images From Portable Devices," in IEEE Access, vol. 8, pp. 195594-195607, 2020, DOI: https://doi.org/10.1109/ACCESS.2020.3033762
- P. L. Vidal, J. de Moura, J. Novo, M. Ortega, "Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19", Expert Systems with Applications , Elsevier, ISSN: 0957-4174, 2021. DOI: https://doi.org/10.1016/j.eswa.2021.114677
FUNDING
Ayudas para rescate de proyectos gallegos ISCIII - Fondo COVID-19
Intelixencia Artificial vs COVID-19: Melloras na detección, avaliación e seguimento personalizado dos afectados Nº Expediente IN845D 2020/38
Investigadores principales: Marcos Ortega Hortas y María del Mar Tomás Carmonal
Operación cofinanciada por la Unión Europea, a través del FONDO EUROPEO DE DESARROLLO REGIONAL (FEDER)
Programa Operativo FEDER GALICIA 2014-2020
