Abstract
Purpose: This paper presents and clinically validates two algorithms for estimating intraocular pressure (IOP) and corneal material behavior using numerical models that consider the fluid-structure interaction between the cornea and the air-puff used in non-contact tonometry.Methods: A novel multi-physics fluid-structure interaction model of the air-puff test was employed in a parametric numerical study simulating human eyes under air-puff pressure with a wide range of central corneal thickness (CCT = 445–645 μm), curvature (R = 7.4–8.4 mm), material stiffness and IOP (10–25 mmHg). Models were internally loaded with IOP using a fluid cavity, then externally with air-puff loading simulated using a turbulent computational fluid dynamics model. Corneal dynamic response parameters were extracted and used in development of two algorithms for IOP and corneal material behavior; fIOP and fSSI, respectively. The two algorithms were validated against clinical corneal dynamic response parameters for 476 healthy participants. The predictions of IOP and corneal material behavior were tested on how they varied with CCT, R, and age.Results: The present study produced a biomechanically corrected estimation of intraocular pressure (fIOP) and a corneal material stiffness parameter or Stress-Strain Index (fSSI), both of which showed no significant correlation with R (p > 0.05) and CCT (p > 0.05). Further, fIOP had no significant correlation with age (p > 0.05), while fSSI was significantly correlated with age (p = 0.001), which was found earlier to be strongly correlated with material stiffness.Conclusion: The present study introduced two novel algorithms for estimating IOP and biomechanical material behavior of healthy corneas in-vivo. Consideration of the fluid structure interaction between the cornea and the air puff of non-contact tonometry in developing these algorithms led to improvements in performance compared with bIOP and SSI.
Highlights
It is of increasing clinical importance to quantify the biomechanical properties of the cornea in vivo
The gold standard of intraocular pressure (IOP) measurements is the Goldmann Applanation Tonometer (GAT), which apply a contact force to a central area of the cornea and when this area flattens, it assumes that the external applied pressure equals the internal IOP (Goldmann and Schmidt, 1957). This measurement technique makes IOP values sensitive to the natural variations in the central corneal thickness (CCT) and stiffness of the corneal tissue and introduces unacceptable inaccuracies (Hemdon et al, 1997; Liu and Roberts, 2005; Eliasy et al, 2018). This was the main motivation for several attempts to provide IOP estimates that are corrected for corneal biomechanics, such as the Ocular Response Analyzer (ORA Reichert Ophthalmic Instruments, Depew, NY) (Luce, 2005), (Montard et al, 2007), and the CorVis ST (OCULUS Optikgeräte GmbH; Wetzlar, Germany) (Joda et al, 2015; Vinciguerra et al, 2016a)
This study was based on a novel multi-physics, fluid-structure interaction (FSI) model of the air-puff test of the Corvis ST on full eye globes subjected to the internal load of IOP
Summary
It is of increasing clinical importance to quantify the biomechanical properties of the cornea in vivo. This measurement technique makes IOP values sensitive to the natural variations in the central corneal thickness (CCT) and stiffness of the corneal tissue and introduces unacceptable inaccuracies (Hemdon et al, 1997; Liu and Roberts, 2005; Eliasy et al, 2018) This was the main motivation for several attempts to provide IOP estimates that are corrected for corneal biomechanics, such as the Ocular Response Analyzer (ORA Reichert Ophthalmic Instruments, Depew, NY) (Luce, 2005), (Montard et al, 2007), and the CorVis ST (OCULUS Optikgeräte GmbH; Wetzlar, Germany) (Joda et al, 2015; Vinciguerra et al, 2016a). This high speed imaging technique enabled accurate measurement of corneal thickness, curvature, and corneal deformation patient-specific parameters, which allowed reliable representation of corneal behavior in numerical modeling to produce the bIOP estimation algorithm
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