Purpose: This study aims to introduce and clinically validate a new algorithm that can determine the biomechanical properties of the human cornea in vivo. Methods: A parametric study was conducted involving representative finite element models of human ocular globes with wide ranges of geometries and material biomechanical behavior. The models were subjected to different levels of intraocular pressure (IOP) and the action of external air puff produced by a non-contact tonometer. Predictions of dynamic corneal response under air pressure were analyzed to develop an algorithm that can predict the cornea's material behavior. The algorithm was assessed using clinical data obtained from 480 healthy participants where its predictions of material behavior were tested against variations in central corneal thickness (CCT), IOP and age, and compared against those obtained in earlier studies on ex-vivo human ocular tissue. Results: The algorithm produced a material stiffness parameter (Stress-Strain Index or SSI) that showed no significant correlation with both CCT (p > 0.05) and IOP (p > 0.05), but was significantly correlated with age (p < 0.01). The stiffness estimates and their variation with age were also significantly correlated (p < 0.01) with stiffness estimates obtained earlier in studies on ex-vivo human tissue. Conclusions: The study introduced and validated a new method for estimating the in vivo biomechanical behavior of healthy corneal tissue. The method can aid optimization of procedures that interfere mechanically with the cornea such as refractive surgeries and introduction of corneal implants.

Purpose of Review In this paper, we prospectively review some artificial intelligence (AI) techniques and models used to enhance clinical decisions for patients with corneal diseases and conditions. Recent Findings Cornea subspeciality was a pioneer in aggregating technology to clinical practice. It provided the ability to early diagnose diseases and improve treatments. Currently, we face the challenge of dealing with a tremendous amount of information from complementary multimodal imaging devices. The analysis of such data for enhancing clinical decisions is perfectly suitable for AI. While AI models are rapidly growing in various fields, some are already available and in use by clinicians, for instance to help in refractive surgery screening. Summary AI models represent a boundless method for helping to deal with and avoid the overload of an extraordinary amount of information provided by advances in complementary diagnosis. It is currently ready to use in refractive surgery screening. The challenge is to coordinate multicentre collaborations in order to build good quality and large data collection to train and improve AI models. AI is an instrument to upturn clinical decision power with many possible applications for ophthalmologists.

Purpose: To introduce a new method to map the mechanical stiffness of healthy and keratoconic corneas.Methods: Numerical modeling based on the finite element method was used to carry out inverse analysis of simulated healthy and keratoconic corneas to determine the regional variation of mechanical stiffness across the corneal surface based on established trends in collagen fibril distribution. The Stress–Strain Index (SSI), developed and validated in an earlier study and presented as a parameter that can estimate the overall stress–strain behavior of corneal tissue, was adopted in this research as a measure of corneal stiffness. The regional variation of SSI across the corneal surface was estimated using inverse analysis while referring to the common features of collagen fibrils’ distribution obtained from earlier x-ray scattering studies. Additionally, for keratoconic corneas, a method relating keratoconic cone features and cornea’s refractive power to the reduction in collagen fibril density inside the cone was implemented in the development of SSI maps. In addition to the simulated cases, the study also included two keratoconus cases, for which SSI maps were developed.Results: SSI values varied slightly across corneal surface in the simulated healthy eyes. In contrast, both simulated and clinical keratoconic corneas demonstrated substantial reductions in SSI values inside the cone. These SSI reductions depended on the extent of the disease and increased with more considerable simulated losses in fibril density in the cone area. SSI values and their regional variation showed little change with changes in IOP, corneal thickness, and curvature.Conclusion: SSI maps provide an estimation of the regional variation of biomechanical stiffness across the corneal surface. The maps could be particularly useful in keratoconic corneas, demonstrating the dependence of corneal biomechanical behavior on the tissue’s microstructure and offering a tool to fundamentally understand the mechanics of keratoconus progression in individual patients.

This paper aims to present a novel full-eye biomechanical material model that incorporates the characteristics of ocular tissues at microstructural level, and use the model to analyse the age-related stiffening in tissue behaviour. The collagen content in ocular tissues, as obtained using X-ray scattering measurements, was represented by sets of Zernike polynomials that covered both the cornea and sclera, then used to reconstruct maps of collagen fibril magnitude and orientation on the three-dimensional geometry of the eye globe. Fine-mesh finite-element (FE) models with eye-specific geometry were built and supported by a user-defined material model (UMAT), which considered the regional variation of fibril density and orientation. The models were then used in an iterative inverse modelling study to derive the material parameters that represent the experimental behaviour of ocular tissues from donors aged between 50 and 90 years obtained in earlierex vivostudies. Sensitivity analysis showed that reducing the number of directions that represented the anisotropy of collagen fibril orientation at each X-ray scattering measurement point from 180 to 16 would have limited and insignificant effect on the FE solution (0.08%). Inverse analysis resulted in material parameters that provided a close match with experimental intraocular pressure–deformation behaviour with a root mean square of error between 3.6% and 4.3%. The results also demonstrated a steady increase in mechanical stiffness in all ocular regions with age. A constitutive material model based on distributions of collagen fibril density and orientation has been developed to enable the accurate representation of the biomechanical behaviour of ocular tissues. The model offers a high level of control of stiffness and anisotropy across ocular globe, and therefore has the potential for use in planning surgical and medical procedures.