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

2024-07-31

arXiv.org

AI Summary

Current methods for predicting myopia progression in children suffer from low accuracy and rely on repeated ophthalmic examinations, exacerbating healthcare inequities. Method: We propose the first deep learning model requiring only a single fundus image and baseline refractive data, integrating temporal convolutional networks with attention mechanisms to enable long-term, quantitative progression prediction and risk stratification—without longitudinal imaging or auxiliary metadata. Contribution/Results: Validated on a multicenter, six-year prospective cohort, our model achieves a mean absolute error of 0.311 D/year in refractive progression prediction; AUCs for incident myopia and high myopia prediction reach 0.944 and 0.995, respectively. By eliminating the need for serial imaging, the approach significantly lowers clinical deployment barriers, facilitates early screening and intervention, and holds promise for mitigating resource disparities and reducing public health burden.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Deep Neural Architectures and Foundation Models
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Predict childhood myopia progression using fundus images and refraction data
• Reduce reliance on subjective clinical assessments for myopia prediction
• Enable early intervention to prevent severe visual impairment in children

Innovation

• Deep learning predicts myopia using fundus images
• Longitudinal study validates high prediction accuracy
• Single time measurement enables cost-effective screening