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Updated: Jan 24, 2026

Three Different Protocols of Corneal Collagen Crosslinking in Keratoconus: Conventional, Accelerated and Iontophoresis
Published on: November 12, 2015
Corneal deformation amplitude analysis for keratoconus detection through compensation for intraocular pressure and
Edileuza Leão1, Tsang Ing Ren2, João M Lyra3
1Centro de Informática (CIn) - Universidade Federal de Pernambuco (UFPE) - v. Jornalista Aníbal Fernandes, Cidade Universitária, 50740-560, Recife, PE, Brazil; Universidade Estadual de Ciências da Saúde de Alagoas (UNCISAL), Brazil; Brazilian Study Group of Artificial Intelligence and Corneal Analysis (BrAIn), Brazil.
Background:
The Corvis ST provides measurements of intraocular pressure (IOP) and a biomechanically-corrected IOP (bIOP). IOP influences corneal deflection amplitude (DA), which may affect the diagnosis of keratoconus. Compensating for IOP in DA values may improve the detection of keratoconus.
Methods:
195 healthy eyes and 136 eyes with keratoconus were included for developing different approaches to distinguish normal and keratoconic corneas using attribute selection and discriminant function. The IOP compensation is proposed by dividing the DA by the IOP values. The first approaches include DA compensated for either IOP or bIOP and other parameters from the deformation corneal response (DCR). Another approach integrated the horizontal corneal thickness profile (HCTP). The best classifiers developed were applied in a validation database of 156 healthy eyes and 87 eyes with keratoconus. Results were compared with the current Corvis Biomechanical Index (CBI).
Results:
The best biomechanical approach used the DA values compensated by IOP (Approach 2) using a linear discriminant function and reached AUC 0.954, with a sensitivity of 88.2% and a specificity of 97.4%. When thickness horizontal profile data was integrated (Approach 4), the best function was the diagquadratic, resulting in an AUC of 0.960, with a sensitivity of 89.7% and a specificity of 96.4%. There was no significant difference in the results between approaches 2 and 4 with the CBI in the training and validation databases.
Conclusions:
By compensating for the IOP, and with the horizontal thickness profile included or excluded, it was possible to generate a classifier based only on biomechanical information with a similar result to the CBI.
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