Deep Learning-Based Longitudinal Prediction of Childhood Myopia Progression Using Fundus Image Sequences and Baseline Refraction Data

• URL: http://arxiv.org/abs/2407.21467v2
• Date: Tue, 15 Apr 2025 16:41:09 GMT
• Title: Deep Learning-Based Longitudinal Prediction of Childhood Myopia Progression Using Fundus Image Sequences and Baseline Refraction Data
• Authors: Mengtian Kang, Yansong Hu, Shuo Gao, Yuanyuan Liu, Hongbei Meng, Xuemeng Li, Xuhang Chen, Hubin Zhao, Jing Fu, Guohua Hu, Wei Wang, Yanning Dai, Arokia Nathan, Peter Smielewski, Ningli Wang, Shiming Li,
• Abstract summary: We introduce a novel, high-accuracy method for quantitatively predicting the myopic trajectory and myopia risk in children.<n>Based on deep learning, our model demonstrated predictive accuracy with an error margin of 0.311D per year and AUC scores of 0.944 and 0.995 for forecasting the risks of developing myopia and high myopia.
• Score: 7.544051156624095
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Childhood myopia constitutes a significant global health concern. It exhibits an escalating prevalence and has the potential to evolve into severe, irreversible conditions that detrimentally impact familial well-being and create substantial economic costs. Contemporary research underscores the importance of precisely predicting myopia progression to enable timely and effective interventions, thereby averting severe visual impairment in children. Such predictions predominantly rely on subjective clinical assessments, which are inherently biased and resource-intensive, thus hindering their widespread application. In this study, we introduce a novel, high-accuracy method for quantitatively predicting the myopic trajectory and myopia risk in children using only fundus images and baseline refraction data. This approach was validated through a six-year longitudinal study of 3,408 children in Henan, utilizing 16,211 fundus images and corresponding refractive data. Our method based on deep learning demonstrated predictive accuracy with an error margin of 0.311D per year and AUC scores of 0.944 and 0.995 for forecasting the risks of developing myopia and high myopia, respectively. These findings confirm the utility of our model in supporting early intervention strategies and in significantly reducing healthcare costs, particularly by obviating the need for additional metadata and repeated consultations. Furthermore, our method was designed to rely only on fundus images and refractive error data, without the need for meta data or multiple inquiries from doctors, strongly reducing the associated medical costs and facilitating large-scale screening. Our model can even provide good predictions based on only a single time measurement. Consequently, the proposed method is an important means to reduce medical inequities caused by economic disparities.

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