Integration of corneal tomography and biomechanical parameters for diagnosis of ectatic disease

Session Details

Session Title: Presented Poster Session: Corneal Biomechanics

Session Date/Time: Sunday 06/09/2015 | 09:30-11:00

Paper Time: 10:00

Venue: Poster Village: Pod 4

First Author: : R.Ambrosio BRAZIL

Co Author(s): :                  

Abstract Details

Purpose:

To investigate the accuracy of corneal biomechanical parameters from Corvis ST for detecting ectatic corneal disease. To test if these metrics provide benefit when combined to Pentacam 3D tomographic data (elevation, curvature and thickness distribution) for detecting ectatic corneal disease.

Setting:

1. Inst. Olhos R. Ambrósio, Rio de Janeiro, Brazil Rio de Janeiro Corneal Tomography and Biomechanical Study Group, Rio de Janeiro, Brazil 2. Rio de Janeiro Corneal Tomography and Biomechanical Study Group, Rio de Janeiro, Brazil 3. BrAIN (Brazilian Artificial Intelligence Study Group for Cornea Analysis

Methods:

Corneal deformation data was obtained from Corvis ST (Oculus, Wetzlar – Germany) and tomographic indices from the Oculus Pentacam HR. The ability of the parameters to distinguish normal (N) and ectatic cases (FFKC and KC) was assessed by receiver operating characteristic (ROC) curve analysis. Linear regression analysis was accomplished to optimize accuracy. 451 normal eyes (N), 241 eyes with keratoconus (KC), and 53 forme fruste keratoconus (FFKC). FFKC criteria was the fellow eye with normal curvature map from patient with clinical keratoconus in the other eye. KC criteria was based on criteria as the Collaborative Longitudinal Evaluation of Keratoconus (CLEK) Study

Results:

Area under the ROC curve (AUC) higher than lower than 0.79 for all Corvis ST derived parameters. The best parameter was BAD-D with AUC of 0. 978. The combination of tomographic parameters and Corvis ST data significantly enhanced the ability to separate normal and ectatic cases. We present two functions which were calculated using different strategies of logistic regression analysis with AUC of 0.986 and 0.991.

Conclusions:

Corneal deformation data (biomechanics) significantly improved the ability of tomography for detecting ectasia, which is more relevant when considering milder forms (FFKC).

Financial Interest:

One of the authors gains financially from product or procedure presented