Keratoconus severity identification using unsupervised machine learning

We developed an unsupervised machine learning algorithm and applied it to big corneal parameters to identify and monitor keratoconus stages. A big dataset of corneal swept source optical coherence tomography (OCT) images of 12,242 eyes acquired from SS-1000 CASIA OCT Imaging Systems in multiple cent...

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Detalles Bibliográficos
Autores principales: Yousefi, Siamak, Yousefi, Ebrahim, Takahashi, Hidenori, Hayashi, Takahiko, Tampo, Hironobu, Inoda, Satoru, Arai, Yusuke, Asbell, Penny
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/PMC6219768/
https://www.ncbi.nlm.nih.gov/pubmed/30399144
http://dx.doi.org/10.1371/journal.pone.0205998

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6219768/
https://www.ncbi.nlm.nih.gov/pubmed/30399144
http://dx.doi.org/10.1371/journal.pone.0205998

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