Machine learning for predicting the treatment effect of orthokeratology in children - Fang, Jianxia; Zheng, Yuxi; Mou, Haochen; Shi, Meipan; Yu, Wangshu; Du, Chixin (Frontiers in Pediatrics, 2023)
Abstract Purpose: Myopia treatment using orthokeratology (ortho-k) results in slowing myopia progression. However, it is not equally effective in all patients. We aimed to predict the treatment effect of ortho-k using a machine-learning-assisted (ML) prediction model. Methods: Out of the 119 patients who started ortho-k treatment between January 1, 2019 and January 1, 2022, 91 met the inclusion criteria and were included in the model. Ocular parameters and clinical characteristics were collected. A logistic regression model with least absolute shrinkage and selection operator regression was used to select factors associated with the treatment effect. Results: Age, baseline axial length, pupil diameter, lens wearing time, time spent outdoors, time spent in near work, white-to-white distance, anterior corneal flat keratometry, and posterior corneal astigmatism were found to be associated with the treatment effect. The decision curve analysis showed beneficial effects. Conclusion: Ocular parameters and clinical characteristics were used to predict the treatment effect of ortho-k. This ML-assisted model may assist ophthalmologists in making clinical decisions for patients, improving myopia control, and predicting the clinical effect of ortho-k treatment via a retrospective non-intervention trial.