Fundus2Globe: Generative AI-Driven 3D Digital Twins for Personalized Myopia Management
Danli Shi, Bowen Liu, Zhen Tian, Yue Wu, Jiancheng Yang, Ruoyu Chen,, Bo Yang, Ou Xiao, Mingguang He

TL;DR
Fundus2Globe is an innovative AI framework that reconstructs personalized 3D eye models from standard 2D fundus images, enabling better myopia management without costly MRI scans.
Contribution
It introduces the first AI method to generate accurate 3D eye models from routine 2D fundus photographs, integrating biomechanical priors and diffusion models.
Findings
Achieves submillimeter accuracy in 3D reconstruction.
Correlates lesions with shape abnormalities validated by MRI.
Demonstrates robust performance across diverse groups.
Abstract
Myopia, projected to affect 50% population globally by 2050, is a leading cause of vision loss. Eyes with pathological myopia exhibit distinctive shape distributions, which are closely linked to the progression of vision-threatening complications. Recent understanding of eye-shape-based biomarkers requires magnetic resonance imaging (MRI), however, it is costly and unrealistic in routine ophthalmology clinics. We present Fundus2Globe, the first AI framework that synthesizes patient-specific 3D eye globes from ubiquitous 2D color fundus photographs (CFPs) and routine metadata (axial length, spherical equivalent), bypassing MRI dependency. By integrating a 3D morphable eye model (encoding biomechanical shape priors) with a latent diffusion model, our approach achieves submillimeter accuracy in reconstructing posterior ocular anatomy efficiently. Fundus2Globe uniquely quantifies how…
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Taxonomy
TopicsAugmented Reality Applications · 3D Shape Modeling and Analysis · Anatomy and Medical Technology
MethodsDiffusion
