Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography-Reference-Cited by-同舟云学术

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Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography

Published:2016-06 Issue:6 Volume:35 Page:827-832
ISSN:0277-3740
Container-title:Cornea
language:en
Short-container-title:

Author:

Ruiz Hidalgo Irene,Rodriguez Pablo,Rozema Jos J.,Ní Dhubhghaill Sorcha,Zakaria Nadia,Tassignon Marie-José,Koppen Carina

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Ophthalmology

Reference23 articles.

1. Longitudinal study of keratoconus progression;Li;Exp Eye Res,2007

2. Incidence and prevalence of keratoconus in Denmark;Nielsen;Acta Ophthalmol Scand,2007

3. Aetiology of keratoconus;Karseras;Br J Ophthalmol,1976

4. Global consensus on keratoconus and ectatic diseases;Gomes;Cornea,2015

5. Riboflavin/ultraviolet-a-induced collagen crosslinking for the treatment of keratoconus;Wollensak;Am J Ophthalmol,2003

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1. Comparisons of corneal biomechanical and higher-order aberrations between thin normal cornea and Forme Fruste keratoconus;2024-07-27

2. Artificial intelligence in ophthalmology: the present and the future;Russian Ophthalmological Journal;2024-07-02

3. KeratoEL: Detection of keratoconus using corneal parameters with ensemble learning;Health Science Reports;2024-06-30

4. Utility of artificial intelligence in the diagnosis and management of keratoconus: a systematic review;Frontiers in Ophthalmology;2024-05-17

5. Comparing the clinical applicability of wavefront phase imaging in keratoconus versus normal eyes;Scientific Reports;2024-05-01

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