Official ESCRS | European Society of Cataract & Refractive Surgeons


Screening for ectasia risk using a new classification (Gatinel-Malet) for higher-order aberrations

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Session Details

Session Title: Ocular Infections & Other Topics

Session Date/Time: Sunday 15/09/2019 | 08:00-10:00

Paper Time: 09:48

Venue: Free Paper Forum: Podium 4

First Author: : A.Saad FRANCE

Co Author(s): :    G. Debellemanière   D. Gatinel                          

Abstract Details


To evaluate the accuracy of an objective method based on new high order aberrations classification (Gatinel-Malet) Placido-disk derived data for the detection of eyes at risk of ectasia. Gatinel-Malet (GM) polynomials separate better than Zernike classification the low- versus High Order Aberrations components because its High Order modes are devoid of linear and quadratic terms.


Rothschild Foundation, Paris, France


119 of 176 patients were included and separated into two groups, normal and forme fruste keratoconus (FFKC), using automated corneal classification software. Normal eyes were classified as negative for keratoconus and keratoconus suspect, and had undergone laser in situ keratomileusis (LASIK) with unremarkable follow-up for 4 years. The FFKC group was composed of 62 topographically normal eyes of patients with keratoconus in the fellow eye. Anterior topographic parameters and corneal wavefront coefficients derived from a new classification (GM) were compared between groups. Receiver operating characteristic (ROC) curves were used to identify cutoff points in discriminating between fellow and normal eyes.


A discriminant function was built combining 4 corneal wavefront variables and 4 Placido variables. The area under the ROC was 0.980 with this 8-variables model. The validation of this function had 77% sensitivity for detecting FFKC and a sensitivity of 100% for detecting keratoconus, with a specificity of 87%.


Gatinel-Malet new high order aberrations classification can identify very early or mild forms of keratoconus undetected by a Placido-based neural network program.

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