Nonlinear Surrogate Models for Emulating Spatial Fields from Multiphysics Models in Support of Glaucoma Diagnosis

Music City Center 

Glaucoma is a major cause of irreversible blindness, affecting millions of people worldwide. In the United States, approximately 2.6% of adults over 40 have glaucoma, with a higher prevalence among Black and Hispanic populations. Traditional clinical measurements often lack sensitivity and specificity, underscoring the need for improved diagnostic tools.
Digital twins based on Multiphysics models provide insight into glaucoma diagnosis by simulating the relationship between changes in intraocular pressure (IOP), blood pressure, tissue perfusion and biomechanical stresses and strains. However, the computational cost of these models limits their clinical applicability in clinical practice. To address this challenge, we develop a spatial statistical emulator for spatial model output that approximates key hemodynamic and biomechanical tissue responses under varying physiological conditions.
We use a first-order singular value decomposition emulator framework that captures the model's primary spatial features using standard clinical input related to ocular physiology. Several nonlinear machine learning methods, including random forests, boosting, multilayer perceptron, and reservoir computing neural models, are explored to construct effective surrogate models. These approaches provide a scalable alternative to direct simulation while preserving predictive accuracy.
The results suggest that nonlinear surrogate models offer an efficient and reliable framework for supporting clinical decision-making in glaucoma diagnosis.

glaucoma diagnosis

intraocular pressure (IOP)

statistical emulation

spatial model

machine learning 

Section on Statistical Learning and Data Science