Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists

BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based di...

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Detalles Bibliográficos
Autores principales: Rajpurkar, Pranav, Irvin, Jeremy, Ball, Robyn L., Zhu, Kaylie, Yang, Brandon, Mehta, Hershel, Duan, Tony, Ding, Daisy, Bagul, Aarti, Langlotz, Curtis P., Patel, Bhavik N., Yeom, Kristen W., Shpanskaya, Katie, Blankenberg, Francis G., Seekins, Jayne, Amrhein, Timothy J., Mong, David A., Halabi, Safwan S., Zucker, Evan J., Ng, Andrew Y., Lungren, Matthew P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245676/
https://www.ncbi.nlm.nih.gov/pubmed/30457988
http://dx.doi.org/10.1371/journal.pmed.1002686

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6245676/
https://www.ncbi.nlm.nih.gov/pubmed/30457988
http://dx.doi.org/10.1371/journal.pmed.1002686

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