Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities

Abstract Purpose To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence t...

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Bibliographic Details
Main Authors: Ce Shi, Mengyi Wang, Tiantian Zhu, Ying Zhang, Yufeng Ye, Jun Jiang, Sisi Chen, Fan Lu, Meixiao Shen
Format: Article
Language:English
Published: BMC 2020-09-01
Series:Eye and Vision
Subjects:
Subclinical keratoconus
Machine learning
Combined-devices
Ultra-high resolution optical coherence tomography
Scheimpflug camera
Online Access:http://link.springer.com/article/10.1186/s40662-020-00213-3

Internet

http://link.springer.com/article/10.1186/s40662-020-00213-3

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