Neural network as a tool to improve clinical outcomes in the treatment of keratoconus with ICRS

Session Details

Session Title: Surgical Cornea

Session Date/Time: Tuesday 25/09/2018 | 16:30-18:00

Paper Time: 16:54

Venue: Room A3, Podium 3

First Author: : J. Alio SPAIN

Co Author(s): :    E. Chorro   J. Bataille   F. Versaci   S. Faini   A. Vega        

Abstract Details

Purpose:

To demonstrate that the neural network ANN can improve the refractive or aberrometric outcomes in the treatment of keratoconus with ICRS.

Setting:

CSO, Florence, Italy. Vissum, Alicante, Spain. Division of Ophthalmology, Miguel Hernández University, Alicante, Spain.

Methods:

ANN is a neural network created to optimize the treatment of keratoconus with ICRS. ANN uses known preoperative and postoperative data to predict the unknown postoperative results from new preoperative data. These data include visual, refractive parameters, corneal topography and pachymetry. In previous works the validation of ANN has been done. In this study a comparison between the refractive and aberrometric outcomes obtained with 8 patients operated with and without the ANN tool was performed. The objective was to compare the result obtained clinically with the best result that ANN predicts by letting it select the best ring.

Results:

The preliminary results showed significant differences (p = 0.011) in the simulated keratometric values in the two groups.

Conclusions:

Improvements in aberrometric outcomes following the ANN recommendations have been found. However, the casuistry is still very limited and cases continue to be recruited.

Financial Disclosure:

-