  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.
