Literature DB >> 10831896

Automated keratoconus detection using the EyeSys videokeratoscope.

P J Chastang1, V M Borderie, S Carvajal-Gonzalez, W Rostène, L Laroche.   

Abstract

PURPOSE: To evaluate the effectiveness of indices derived from the EyeSys System 2000 in detecting keratoconic corneas.
SETTING: Department of Ophthalmology, Hôpital Saint Antoine, Paris VI University, Paris, France.
METHODS: Topographies of 208 corneas were evaluated. The corneas were from 8 groups of patients classified by the following diagnoses: normal, regular astigmatism, cataract, radial keratotomy, photorefractive keratectomy, myopic keratomileusis, penetrating keratoplasty (PKP), and keratoconus. Nine statistical indices derived from EyeSys data, 2 Holladay Diagnosis Summary indices (coefficient of uniformity and coefficient of asphericity [Asph]), and our refractive power symmetry index were studied. A training set of 104 corneas was used to determine the most efficient threshold value of each index based on sensitivity and specificity curves. Decision trees combining 2 indices were generated. Sensitivity and specificity were calculated in a validation set composed of the remaining 104 corneas.
RESULTS: Based on the results of the training set, the optimum indices were SDSD (standard deviation of the standard deviations of the radii of curvature of each ring) and Asph. In the validation set, the decision tree using these indices featured 88.5% sensitivity and 94.9% specificity; the 4 false-positive cases were in corneas in the PKP group of patients.
CONCLUSIONS: Clinically apparent keratoconus can be detected among normal corneas and irregular corneal shape patterns using the EyeSys System 2000 data and a decision tree combining 2 indices.

Entities:  

Mesh:

Year:  2000        PMID: 10831896     DOI: 10.1016/s0886-3350(00)00303-5

Source DB:  PubMed          Journal:  J Cataract Refract Surg        ISSN: 0886-3350            Impact factor:   3.351


  11 in total

1.  Automated decision tree classification of corneal shape.

Authors:  Michael D Twa; Srinivasan Parthasarathy; Cynthia Roberts; Ashraf M Mahmoud; Thomas W Raasch; Mark A Bullimore
Journal:  Optom Vis Sci       Date:  2005-12       Impact factor: 1.973

2.  Automated keratoconus detection using height data of anterior and posterior corneal surfaces.

Authors:  Kenichiro Bessho; Naoyuki Maeda; Teruhito Kuroda; Takashi Fujikado; Yasuo Tano; Tetsuro Oshika
Journal:  Jpn J Ophthalmol       Date:  2006 Sep-Oct       Impact factor: 2.447

3.  Four discriminant models for detecting keratoconus pattern using Zernike coefficients of corneal aberrations.

Authors:  Makoto Saika; Naoyuki Maeda; Yoko Hirohara; Toshifumi Mihashi; Takashi Fujikado; Kohji Nishida
Journal:  Jpn J Ophthalmol       Date:  2013-08-27       Impact factor: 2.447

4.  Topographic determination of corneal asphericity as a function of age, gender, and refractive error.

Authors:  Negareh Yazdani; Leila Shahkarami; Hadi OstadiMoghaddam; Asieh Ehsaei
Journal:  Int Ophthalmol       Date:  2016-09-06       Impact factor: 2.031

5.  Topographic typology in a consecutive series of refractive surgery candidates.

Authors:  Seyed-Farzad Mohammadi; Vahid Mohammadzadeh; Sakineh Kadivar; Amir-Houshang Beheshtnejad; Amir Hossein Norooznezhad; Seyed-Hassan Hashemi
Journal:  Int Ophthalmol       Date:  2017-07-04       Impact factor: 2.031

6.  Corneal elevation topography: best fit sphere, elevation distance, asphericity, toricity, and clinical implications.

Authors:  Damien Gatinel; Jacques Malet; Thanh Hoang-Xuan; Dimitri T Azar
Journal:  Cornea       Date:  2011-05       Impact factor: 2.651

Review 7.  Corneal topography in keratoconus: state of the art.

Authors:  F Cavas-Martínez; E De la Cruz Sánchez; J Nieto Martínez; F J Fernández Cañavate; D G Fernández-Pacheco
Journal:  Eye Vis (Lond)       Date:  2016-02-22

8.  Keratoconus severity identification using unsupervised machine learning.

Authors:  Siamak Yousefi; Ebrahim Yousefi; Hidenori Takahashi; Takahiko Hayashi; Hironobu Tampo; Satoru Inoda; Yusuke Arai; Penny Asbell
Journal:  PLoS One       Date:  2018-11-06       Impact factor: 3.240

Review 9.  Assessing progression of keratoconus: novel tomographic determinants.

Authors:  Joshua K Duncan; Michael W Belin; Mark Borgstrom
Journal:  Eye Vis (Lond)       Date:  2016-03-11

10.  Evaluating the Performance of Various Machine Learning Algorithms to Detect Subclinical Keratoconus.

Authors:  Ke Cao; Karin Verspoor; Srujana Sahebjada; Paul N Baird
Journal:  Transl Vis Sci Technol       Date:  2020-04-24       Impact factor: 3.283

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