Background: Keratoconus is a common disease characterized by progressive corneal slimming and steepening. The disease progression is generally accompanied by the significant decline in the vision, aggravation of irregular corneal astigmatism and the resultant decrease in the patient's quality of life. One of the successful treatments for Keratoconus is the corneal ring implantation. The prediction of post-surgical visual characteristics has been considered in this study to assist the ophthalmologist in appropriately choosing surgery candidates.
Materials and Methods: In this study, the set of data collected from numerous tests of visual characteristics performed before and after implanting the keraring and myoring rings has been utilized. By using MATLAB software, the visual characteristics of keratoconus patients after implant­ing the rings have been estimated through correct training of the proposed neural networks. The characteristics include: uncorrected visual acuity (UCVA), sphere (SPH), astigmatism (Ast), orientation of astigmatism (Axis), and best corrected visual acuity (BCVA).
Results: In this research, for the first time, the visual characteristics of keratoconus patients six and twelve months after implanting the corneal ring have been predicted with the mean error of 9.51% manipulating a novel neural-network-based method.
Conclusion: The results indicate the precision and accuracy of the proposed method in predicting the visual characteristics of keratoconus patients after implanting the corneal rings. The ophthal­mologist could precisely choose the right candidate for surgery amongst his patients based on the estimated characteristics.