A new Method for the in vivo identification of material properties of the human eye. Feasibility analysis based on synthetic data

TL;DR

This paper introduces a rapid, non-contact method for in vivo biomechanical property identification of the human cornea using synthetic data, demonstrating promising accuracy and real-time potential despite some noise sensitivity.

Contribution

A novel approach combining the equilibrium gap method with mechanical morphing for fast, in vivo corneal material property identification using synthetic data.

Findings

Method achieves relevant accuracy in parameter identification.

Operates in near real-time on standard hardware.

Sensitivity to small noise levels affects accuracy.

Abstract

This paper proposes a new method for in vivo and almost real-time identification of biomechanical properties of the human cornea based on non-contact tonometer data. Further goal is to demonstrate the method's functionality based on synthetic data serving as reference. For this purpose, a finite element model of the human eye is constructed to synthetically generate displacement full-fields from different datasets with keratoconus-like degradations. Then, a new approach based on the equilibrium gap method (EGM) combined with a mechanical morphing approach is proposed and used to identify the material parameters from virtual test data sets. In a further step, random absolute noise is added to the virtual test data to investigate the sensitivity of the new approach to noise. As a result, the proposed method shows a relevant accuracy in identifying material parameters based on displacement full fields. At the same time, the method turns out to work almost in real-time (order of a few minutes on a regular work station) and is thus much faster than inverse problems solved by typical forward approaches. On the other hand, the method shows a noticeable sensitivity to rather small noise amplitudes. However, analysis show that the accuracy is sufficient for the identification of diseased tissue properties.