Yafteh

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.

Background: Keratoconus is a common complication among corneal defects. As a result of expeditious and extensive progress of medical science in recent decades, corneal ring implantation has turned into a successful surgical procedure to correct the vision of Keratoconus patients; however, selecting the right patient is essential in the success of the operation. The prediction of corneal condition or, more precisely, the prediction of corneal topographic indices after implanting the ring has been taken into consideration in the present study.
Materials and Methods: Neural network framework is one of the optimal methods for the modeling and prediction. In this study, corneal topographic indices of patients have been predicted 6 and 12 months after the ring implantation for the first time using the multilayer feed forward neural network. The study has focused on predicting corneal topographic indices that are applicable to Keratoconus diagnosis and progression using MATLAB software. Therefore, the statistical data of a number of patients, including the effective indices of the cornea topography before and after implanting the ring, were collected.
Results: Using the collected data, neural network models have been developed and the corneal topographic indices have been predicted 6 and 12 months after the implantation of keraring and myoring rings. The mean error of 7.22% is achieved for the four trained neural network models. 
Conclusion: Choosing the right surgical candidate is one of the primary concerns of ophthalmologists. The results indicate the great capability of neural networks in assisting ophthalmologists to select right surgical candidates through predicting corneal topographic indices after the ring implantation.