Prediction of Myopia Eye Axial Elongation With Orthokeratology Treatment via Dense I2I Based Corneal Topography Change Analysis

While orthokeratology (OK) has shown effective to slow the progression of myopia, it remains unknown how spatially distributed structural stress/tension applying to different regions affects the change of corneal geometry, and consecutive the outcome of myopia control, at fine-grained detail. Acknowledging that the underlying working mechanism of OK lens is essentially mechanics induced refractive parameter reshaping, in this study, we develop a novel mechanics rule guided deep image-to-image learning framework, which densely predicts patient's corneal topography change according to treatment parameters (lens geometry, wearing time, physiological parameters, etc.), and consecutively predicts the influence on eye axial length change after OK treatment. Encapsulated in a U-shaped multi-resolution map-to-map architecture, the proposed model features two major components. First, geometric and wearing parameters of OK lens are spatially encoded with convolutions to form a multi-channel input volume/tensor for latent encodings of external stress/tension applied to different regions of cornea. Second, these external latent force maps are progressively down-sampled and injected into this multi-scale architecture for predicting the change of corneal topography map. At each feature learning layer, we formally derive a mathematic framework that simulates the physical process of corneal deformation induced by lens-to-cornea interaction and corneal internal tension, which is reformulated into parameter learnable cross-attention/self-attention modules in the context of transformer architecture. A total of 1854 eyes of myopia patients are included in the study and the results show that the proposed model precisely predicts corneal topography change with a high PSNR as 28.45dB, as well as a significant accuracy gain for axial elongation prediction (i.e., 0.0276 in MSE). It is also demonstrated that our method provides interpretable associations between various OK treatment parameters and the final control effect. Our project code package is available at https://github.com/Rongdingyi/PhyIntNet.