A machine learning framework for automated diagnosis and computer-assisted planning in plastic and reconstructive surgery

Current computational tools for planning and simulation in plastic and reconstructive surgery lack sufficient precision and are time-consuming, thus resulting in limited adoption. Although computer-assisted surgical planning systems help to improve clinical outcomes, shorten operation time and reduc...

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Detalles Bibliográficos
Autores principales: Knoops, Paul G. M., Papaioannou, Athanasios, Borghi, Alessandro, Breakey, Richard W. F., Wilson, Alexander T., Jeelani, Owase, Zafeiriou, Stefanos, Steinbacher, Derek, Padwa, Bonnie L., Dunaway, David J., Schievano, Silvia
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/PMC6753131/
https://www.ncbi.nlm.nih.gov/pubmed/31537815
http://dx.doi.org/10.1038/s41598-019-49506-1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6753131/
https://www.ncbi.nlm.nih.gov/pubmed/31537815
http://dx.doi.org/10.1038/s41598-019-49506-1

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