Ocular condition prognosis in Keratoconus patients after corneal ring implantation using artificial neural networks

Omid Mehdizadeh Dastjerdi; Marjan Bakhtiarnia; Mohammadreza Yazdchi; Keivan Maghooli; Fardad Farokhi; Khosrow Jadidi

doi:10.1016/j.heliyon.2023.e19411

Ocular condition prognosis in Keratoconus patients after corneal ring implantation using artificial neural networks

Heliyon. 2023 Aug 28;9(9):e19411. doi: 10.1016/j.heliyon.2023.e19411. eCollection 2023 Sep.

Authors

Omid Mehdizadeh Dastjerdi¹, Marjan Bakhtiarnia², Mohammadreza Yazdchi¹, Keivan Maghooli², Fardad Farokhi³, Khosrow Jadidi⁴

Affiliations

¹ Department of Biomedical Engineering, Faculty of Engineering, University of Isfahan, Isfahan, Iran.
² Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.
³ Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran.
⁴ Visual Health Research Center, Semnan University of Medical Sciences and Health Services, Semnan, Iran.

Abstract

The common disorder, Keratoconus (KC), is distinguished by cumulative corneal slimming and steepening. The corneal ring implantation has become a successful surgical procedure to correct the KC patient's vision. The determination of suitable patients for the surgery alternative is among the paramount concerns of ophthalmologists. To reduce the burden on them and enhance the treatment, this research aims to previse the ocular condition of KC patients after the corneal ring implantation. It focuses on predicting post-surgical corneal topographic indices and visual characteristics. This study applied an efficacious artificial neural network approach to foretell the aforementioned ocular features of KC subjects 6 and 12 months after implanting KeraRing and MyoRing based on the accumulated data. The datasets are composed of sufficient numbers of corneal topographic maps and visual characteristics recorded from KC patients before and after implanting the rings. The visual characteristics under study are uncorrected visual acuity (UCVA), sphere (SPH), astigmatism (Ast), astigmatism orientation (Axe), and best corrected visual acuity (BCVA). In addition, the statistical data of multiple KC subjects were registered, including three effective indices of corneal topography (i.e., Ast, K-reading, and pachymetry) pre- and post-ring embedding. The outcomes represent the contribution of practical training of the introduced models to the estimation of ocular features of KC subjects following the implantation. The corneal topographic indices and visual characteristics were estimated with mean errors of 7.29% and 8.60%, respectively. Further, the errors of 6.82% and 7.65% were respectively realized for the visual characteristics and corneal topographic indices while assessing the predictions by the leave-one-out cross-validation (LOOCV) procedure. The results confirm the great potential of neural networks to guide ophthalmologists in choosing appropriate surgical candidates and their specific intracorneal rings by predicting post-implantation ocular features.

Keywords: Corneal topographic indices; KeraRing/MyoRing implantation; Neural networks; Post-surgical prediction; Visual characteristics.