Chief Supervisor

Dr SHI Danli

Project Title

Synopsis

Personalised myopia management requires reliable estimates of how an individual child’s eyes will grow and respond to treatment. This project aims to develop and validate Myopia Twin, a digital twin framework for modelling individual myopia trajectories and informing clinical decisions.

The framework will integrate ocular imaging, biometric measurements and longitudinal clinical records to create patient-specific digital representations of eye structure and growth. By linking three-dimensional eye modelling with longitudinal prediction, it will estimate axial elongation and refractive progression under different management strategies. Each digital twin will be
updated as follow-up data become available, allowing predictions to reflect the child’s evolving ocular characteristics and observed treatment response.

Using retrospective cohorts, independent validation datasets and prospective clinical evaluation, the research will assess anatomical fidelity, predictive accuracy, generalisability and clinicalutility. It will examine whether these dynamically updated models improve on baseline-only predictions and support treatment selection, timely treatment adjustment and personalised
follow-up. The ultimate goal is to move beyond static risk assessment towards myopia care thatadapts to each child’s changing clinical needs.