Cornea

Random Forest, XGBoost, and LightGBM surrogate models predict third-order Ogden corneal parameters from apex displacement data, evaluated with a physics-informed stress-stretch metric.

Oral Presentation in 32nd National Conference and the 10th International Conference on Iranian Biomedical Engineering on Ensemble Learning-Based Surrogate Models for Non-Invasive Estimation of Corneal Mechanical Properties

A neural network-based inverse model was developed to predict Ogden material parameters from corneal apex displacement data.

Oral Presentation in 2nd Regional Symposium on Science and Technology on Neural Network-Based Inverse Model for Non-Invasive Estimation of Corneal Mechanical Properties