Posters

Artificial inteligence to guide intracorneal ring segments for keratoconus treatment

Poster Details

First Author: A.Vega Estrada SPAIN

Co Author(s):    C. Fariselli   J. Alio                 

Abstract Details

Purpose:

To analyze the clinical outcomes of an artificial neural network (ANN) designed for improving the predictability of intracorneal ring segments (ICRS) in keratoconus.

Setting:

Vissum Miranza, Spain.

Methods:

Comparative, clinical study including 20 keraRED NEtoconic eyes implanted with ICRS guided by ANN (ANN group) and 20 keratoconic eyes implanted with ICRS guided by manufacturer’s nomograms (nomogram group). Uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA), in decimal scale, manifest refraction, corneal topography, aberrometry, and volume analysis (Sirius System. CSO, Firenze, Italy) were performed in both groups. Cases were followed during 3 months.

Results:

The spherical equivalent and the keratometric values decreased significantly in both groups. The CDVA improved from 0.60 ± 0.23 pre-operatively to 0.73 ± 0.21 post-operatively in the ANN group (p < 0.005), and from 0.54 ± 0.19 pre-operatively to 0.62 ± 0.19 post-operatively in the nomogram group (p < 0.01), with statistically significant difference between the two groups (p < 0.05), being better in the ANN group. Coma-like aberrations decreased significantly in the ANN group, while in the nomogram group they did not change significantly, but no statistically significant difference was found between the two groups.

Conclusions:

ANN to guide ICRS provides an increase in the visual acuity, reduction in the spherical equivalent and improvement in the optical quality of keratoconus patients. ANN gives better results when compared with the manufacturer’s nomograms in terms of better corrected vision and reduction of corneal aberrations.

Financial Disclosure:

None