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ORIGINAL RESEARCH article

Front. Med.
Sec. Ophthalmology
Volume 11 - 2024 | doi: 10.3389/fmed.2024.1490525
This article is part of the Research Topic New Concepts, Advances, and Future Trends in Clinical Research on Eye Diseases View all articles

An Integrative Predictive Model for Orthokeratology Lens Decentration Based on Diverse Metrics

Provisionally accepted
Kunhong Xiao Kunhong Xiao 1Wenrui Lu Wenrui Lu 2Xuemei Zhang Xuemei Zhang 2Shenghua Lin Shenghua Lin 1Jingting Wei Jingting Wei 1Xiangjie Lin Xiangjie Lin 1Qingyuan Cai Qingyuan Cai 1Jiawen Lin Jiawen Lin 3Li Li Li Li 2*
  • 1 Fujian Medical University, Fuzhou, China
  • 2 Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, , China., Fuzhou, China
  • 3 Fuzhou University, Fuzhou, Fujian Province, China

The final, formatted version of the article will be published soon.

    To develop a predictive model for orthokeratology (Ortho-K) lens decentration one month after wear.Methods: This study included myopic children who were fitted with Ortho-K lenses at Fujian Provincial Hospital between December 2022 and May 2024. Corneal topography parameters and other relevant metrics were collected pre-and post-treatment. Feature selection was conducted using univariate logistic regression and Lasso regression analysis. A machine learning approach was used to develop multiple predictive models, including Decision Tree, Logistic Regression, Multilayer Perceptron, Random Forest, and Support Vector Machine. Model performance was evaluated using accuracy, sensitivity, specificity, ROC curves, DCA curves, and calibration curves. SHAP values were employed to interpret the models.The Logistic Regression model demonstrated the best predictive performance, with an AUC of 0.82 (95% CI: 0.69-0.95), accuracy of 77.59%, sensitivity of 85%, and specificity of 61.11%. The most significant predictors identified were age, 8mm sag height difference, 5mm Kx1, and 7mm Kx2. SHAP analysis confirmed the importance of these features, particularly the 8mm sag height difference.The Logistic Regression model successfully predicted the risk of Ortho-K lens decentration using key corneal morphological metrics and age. This model provides valuable support for clinicians in optimizing Ortho-K lens fitting strategies, potentially reducing the risk of adverse outcomes and improving the quality of vision for patients. Further validation in clinical settings is recommended.

    Keywords: Orthokeratology1, Lens Decentration2, predictive model3, Logistic Regression model4, Myopia5. 2

    Received: 03 Sep 2024; Accepted: 23 Sep 2024.

    Copyright: © 2024 Xiao, Lu, Zhang, Lin, Wei, Lin, Cai, Lin and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Li Li, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, , China., Fuzhou, China

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