Abstract
BackgroundAppropriate sizing of the implantable collamer lens (ICL) and accurate prediction of the vault are crucial prior to surgery. However, sometimes, the vault value is higher or lower than predicted, necessitating reoperation. The present study aimed to develop neural networks for improving predictions of vault values following ICL implantation based on preoperative biometric data.MethodsThis retrospective study included 137 eyes of 74 patients with ICLs. Linear regression and neural network analyses were used to examine the relationship between vault values at the 6-month follow-up and preoperative parameters (e.g., ICL characteristics and biometrics).ResultsLinear regression analysis revealed that vault values were correlated with five variables: ICL size, anterior chamber depth (ACD), angle-to-angle (ATA), white-to-white (WTW), and lens thickness (LT) (adjusted R2 = 0.411). Inclusion of more input variables was associated with better performance in the neural network analysis. The degree of fit when all 11 variables were included in the neural network model was close to 1 (R2 = 0.98). R2 values for the quaternary neural network model enrolling four input variables (ICL size, ATA, ACD, and LT) reached 0.90.ConclusionsA neural network equation including the ICL size and biometric parameters of the anterior segment (ATA, ACD, and LT) can be used to predict the postoperative vault, aiding in the selection of an appropriate ICL size and reducing the need for reoperation after surgery.
Highlights
Appropriate sizing of the implantable collamer lens (ICL) and accurate prediction of the vault are crucial prior to surgery
Previous studies have indicated that the ICL vault can be roughly modeled using linear regression (R2 0.41 in multiple regression analysis) of various preoperative biometric combinations.there is no standard formula for predicting the vault. This is the first study to demonstrate that a neural network model considering 11 biometric factors can be used for excellent modeling of the ICL vault, suggesting that the relationship between the ICL vault and biometric factors is nonlinear
Recent studies utilizing regression analyses have reported that the postoperative vault can only be explained in approximately 41.0% of cases [6,7,8,9,10]
Summary
Appropriate sizing of the implantable collamer lens (ICL) and accurate prediction of the vault are crucial prior to surgery. The present study aimed to develop neural networks for improving predictions of vault values following ICL implantation based on preoperative biometric data. Results Linear regression analysis revealed that vault values were correlated with five variables: ICL size, anterior chamber depth (ACD), angle-to-angle (ATA), white-to-white (WTW), and lens thickness (LT) (adjusted R2 = 0.411). R2 values for the quaternary neural network model enrolling four input variables (ICL size, ATA, ACD, and LT) reached 0.90. Conclusions A neural network equation including the ICL size and biometric parameters of the anterior segment (ATA, ACD, and LT) can be used to predict the postoperative vault, aiding in the selection of an appropriate ICL size and reducing the need for reoperation after surgery. Sometimes the vault value is higher or lower than predicted, necessitating reoperation
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Graefe's Archive for Clinical and Experimental Ophthalmology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
