Deep learning versus human graders for classifying diabetic retinopathy severity in a nationwide screening program

Deep learning algorithms have been used to detect diabetic retinopathy (DR) with specialist-level accuracy. This study aims to validate one such algorithm on a large-scale clinical population, and compare the algorithm performance with that of human graders. A total of 25,326 gradable retinal images...

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Detalles Bibliográficos
Autores principales: Ruamviboonsuk, Paisan, Krause, Jonathan, Chotcomwongse, Peranut, Sayres, Rory, Raman, Rajiv, Widner, Kasumi, Campana, Bilson J. L., Phene, Sonia, Hemarat, Kornwipa, Tadarati, Mongkol, Silpa-Archa, Sukhum, Limwattanayingyong, Jirawut, Rao, Chetan, Kuruvilla, Oscar, Jung, Jesse, Tan, Jeffrey, Orprayoon, Surapong, Kangwanwongpaisan, Chawawat, Sukumalpaiboon, Ramase, Luengchaichawang, Chainarong, Fuangkaew, Jitumporn, Kongsap, Pipat, Chualinpha, Lamyong, Saree, Sarawuth, Kawinpanitan, Srirut, Mitvongsa, Korntip, Lawanasakol, Siriporn, Thepchatri, Chaiyasit, Wongpichedchai, Lalita, Corrado, Greg S., Peng, Lily, Webster, Dale R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550283/
https://www.ncbi.nlm.nih.gov/pubmed/31304372
http://dx.doi.org/10.1038/s41746-019-0099-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550283/
https://www.ncbi.nlm.nih.gov/pubmed/31304372
http://dx.doi.org/10.1038/s41746-019-0099-8

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