Amini, Rouzbeh

Abstract In this study, we quantified differences in iris stiffness between female and male subjects in healthy and postlaser peripheral iridotomy (post-LPI) groups using an image-based inverse modeling approach. We analyzed anterior segment optical coherence tomography (AS-OCT) images from 25 participants across four groups. Finite element models were created using **solidworks, **abaqus, and a custom C program, modeling the iris as a neo-Hookean material. We found that post-LPI females had significantly higher normalized elastic modulus (E′=3.81±1.74) than healthy females (E′=0.92±0.31,p=0.004), while no significant difference was observed in males. Post-LPI females also showed significantly higher stiffness than post-LPI males (p=0.003). Here, p denotes the probability value, with p<0.05 considered statistically significant. Our findings suggest that sex-based differences in iris biomechanics may contribute to the higher susceptibility of females to primary angle-closure disease. Despite the small sample size, this preliminary study highlights the need for larger, sex-stratified investigations into glaucoma pathophysiology.

Quantifying the mechanical properties of the iris is important, as it provides insight into the pathophysiology of glaucoma. Recent ex vivo studies have shown that the mechanical properties of the iris are different in glaucomatous eyes as compared to normal ones. Notwithstanding the importance of the ex vivo studies, such measurements are severely limited for diagnosis and preclude development of treatment strategies. With the advent of detailed imaging modalities, it is possible to determine the in vivo mechanical properties using inverse finite element (FE) modeling. An inverse modeling approach requires an appropriate objective function for reliable estimation of parameters. In the case of the iris, numerous measurements such as iris chord length (CL) and iris concavity (CV) are made routinely in clinical practice. In this study, we have evaluated five different objective functions chosen based on the iris biometrics (in the presence and absence of clinical measurement errors) to determine the appropriate criterion for inverse modeling. Our results showed that in the absence of experimental measurement error, a combination of iris CL and CV can be used as the objective function. However, with the addition of measurement errors, the objective functions that employ a large number of local displacement values provide more reliable outcomes.