Application of machine learning algorithms to improve the accuracy of laser vision correction outcomes

Abstract

Laser vision correction (LVC) is a high-technology method of correcting refractive errors, the effectiveness of which largely depends on the precision of preoperative diagnostics, the individualization of surgical parameters, and the accurate prediction of postoperative outcomes. In recent years, machine learning (ML) algorithms have been increasingly integrated at various stages of the LVC process, contributing to the transformation of ophthalmic surgical practice toward a more personalized approach. This literature review aims to systematize current advancements in the application of ML in laser refractive surgery. In the preoperative phase, ML enables the accurate selection of suitable candidates, the identification of contraindications (including keratoconus), the choice of optimal surgical technique, and the calculation of ablation parameters. Employed algorithms, such as random forest, XGBoost, and deep neural networks, exhibit excellent sensitivity and specificity, frequently outperforming conventional clinical methods. In the postoperative period, ML is utilized to predict visual outcomes, assess the risk of refractive regression, and determine the likelihood of enhancement procedures. Despite its high predictive accuracy, the current use of artificial intelligence (AI) remains constrained by the retrospective nature of available data, the lack of large-scale clinical validation, and the ongoing need for integration into clinical workflows. This review underscores the importance of further research to establish standardized protocols, ensure interoperability, and achieve regulatory compliance for AI-based solutions in ophthalmology.