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<p>Myopia is a major global health concern. To enable precision myopia management, we developed a Transformer-based artificial intelligence (AI) model, the Myopia Progression Predictive Model (MPPM), comprising two modules: the Natural Progression Module (NPM) for predicting untreated myopia progression and the Intervention Progression Module (IPM) for forecasting progression under specific interventions. NPM was trained on 1,109,827 refractive records from 304,353 children and adolescents, achieving high predictive accuracy for future spherical equivalent (SE) and axial length (AL) over a 10-year period. In the internal test set, SE prediction reached <i>R</i>² = 0.94, MAE = 0.35D; for AL, <i>R</i>² = 0.91, MAE = 0.16 mm. Comparable performance was observed in external validation. IPM was trained on four intervention cohorts (0.01% atropine, orthokeratology, peripheral defocus spectacles, and repeated low-level red light [RLRL] therapy) using a Transformer-based causal machine learning framework, enabling individualized estimation of treatment effects. It accurately predicted myopia changes under each intervention (SE: <i>R</i>² &gt; 0.88, MAE &lt; 0.45D; AL: <i>R</i>² &gt; 0.80, MAE &lt; 0.31 mm). Among the interventions, RLRL slightly reversed myopia progression, whereas the others slowed myopia progression. MPPM demonstrates strong promise as an AI-driven platform for personalized prediction and optimization of pediatric myopia management.</p>

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AI-guided personalized predictions on myopia progression and interventions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月12日

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  • Sian Liu,
  • Yuxing Lu,
  • Xiaoman Li,
  • Xiaoniao Chen,
  • Zhuo Sun,
  • Gen Li,
  • Kai Wang,
  • Wei Wu,
  • Hui Xu,
  • Hongyi Li,
  • Changxi Hu,
  • Zixing Zou,
  • Miao Zhang,
  • Xuan Zhang,
  • Wenyang Lu,
  • Yun Yin,
  • Jia Qu,
  • Kang Zhang,
  • Jie Chen

摘要

Myopia is a major global health concern. To enable precision myopia management, we developed a Transformer-based artificial intelligence (AI) model, the Myopia Progression Predictive Model (MPPM), comprising two modules: the Natural Progression Module (NPM) for predicting untreated myopia progression and the Intervention Progression Module (IPM) for forecasting progression under specific interventions. NPM was trained on 1,109,827 refractive records from 304,353 children and adolescents, achieving high predictive accuracy for future spherical equivalent (SE) and axial length (AL) over a 10-year period. In the internal test set, SE prediction reached R² = 0.94, MAE = 0.35D; for AL, R² = 0.91, MAE = 0.16 mm. Comparable performance was observed in external validation. IPM was trained on four intervention cohorts (0.01% atropine, orthokeratology, peripheral defocus spectacles, and repeated low-level red light [RLRL] therapy) using a Transformer-based causal machine learning framework, enabling individualized estimation of treatment effects. It accurately predicted myopia changes under each intervention (SE: R² > 0.88, MAE < 0.45D; AL: R² > 0.80, MAE < 0.31 mm). Among the interventions, RLRL slightly reversed myopia progression, whereas the others slowed myopia progression. MPPM demonstrates strong promise as an AI-driven platform for personalized prediction and optimization of pediatric myopia management.