Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs using Convolutional Siamese Neural Networks

PURPOSE: To develop an automated measure of COVID-19 pulmonary disease severity on chest radiographs (CXRs), for longitudinal disease tracking and outcome prediction. MATERIALS AND METHODS: A convolutional Siamese neural network-based algorithm was trained to output a measure of pulmonary disease se...

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Detalles Bibliográficos
Autores principales: Li, Matthew D., Arun, Nishanth Thumbavanam, Gidwani, Mishka, Chang, Ken, Deng, Francis, Little, Brent P., Mendoza, Dexter P., Lang, Min, Lee, Susanna I., O’Shea, Aileen, Parakh, Anushri, Singh, Praveer, Kalpathy-Cramer, Jayashree
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Radiological Society of North America 2020
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392327/
https://www.ncbi.nlm.nih.gov/pubmed/33928256
http://dx.doi.org/10.1148/ryai.2020200079

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392327/
https://www.ncbi.nlm.nih.gov/pubmed/33928256
http://dx.doi.org/10.1148/ryai.2020200079

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