Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs

We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of reporting and workflow efficacy. DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based...

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Detalles Bibliográficos
Autores principales: Nam, Ju Gang, Kim, Minchul, Park, Jongchan, Hwang, Eui Jin, Lee, Jong Hyuk, Hong, Jung Hee, Goo, Jin Mo, Park, Chang Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: European Respiratory Society 2021
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134811/
https://www.ncbi.nlm.nih.gov/pubmed/33243843
http://dx.doi.org/10.1183/13993003.03061-2020

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8134811/
https://www.ncbi.nlm.nih.gov/pubmed/33243843
http://dx.doi.org/10.1183/13993003.03061-2020

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