Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

Intracranial hemorrhage (ICH) requires prompt diagnosis to optimize patient outcomes. We hypothesized that machine learning algorithms could automatically analyze computed tomography (CT) of the head, prioritize radiology worklists and reduce time to diagnosis of ICH. 46,583 head CTs (~2 million ima...

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Detalles Bibliográficos
Autores principales: Arbabshirani, Mohammad R., Fornwalt, Brandon K., Mongelluzzo, Gino J., Suever, Jonathan D., Geise, Brandon D., Patel, Aalpen A., Moore, Gregory J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550144/
https://www.ncbi.nlm.nih.gov/pubmed/31304294
http://dx.doi.org/10.1038/s41746-017-0015-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6550144/
https://www.ncbi.nlm.nih.gov/pubmed/31304294
http://dx.doi.org/10.1038/s41746-017-0015-z

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