Keratoconus severity identification using unsupervised machine learning.

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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Bibliographic Details
Main Authors: Siamak Yousefi, Ebrahim Yousefi, Hidenori Takahashi, Takahiko Hayashi, Hironobu Tampo, Satoru Inoda, Yusuke Arai, Penny Asbell
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2018-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC6219768?pdf=render

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http://europepmc.org/articles/PMC6219768?pdf=render

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