ArticleFrontiers in public health2026
Myo-ODE: continuous-time trajectory reconstruction and risk prediction of high myopia via neural ordinary differential equations.
Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The global surge in childhood myopia necessitates robust screening tools for early risk stratification; however, conventional predictive models often struggle with irregular follow-up intervals and fail to capture the continuous nature of refractive development. We propose Myo-ODE, a continuous-time framework based on Neural Ordinary Differential Equations (Neural ODEs) to predict high myopia risk. Unlike traditional discrete machine learning models, Myo-ODE parameterizes the derivative of the refractive state, allowing myopia progression to be represented as a continuous latent dynamic flow. This architecture explicitly accommodates non-uniform screening intervals and supports temporal interpolation and cautious short-term extrapolation within the observed follow-up horizon. Evaluated on a longitudinal dataset (
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