Typical localised element-specific finite element anterior eye model

Towler, Joseph; Consejo, Alejandra; Zhou, Dong; Romano, Vito; Levis, Hannah J.; Boote, Craig; Elsheikh, Ahmed; Geraghty, Brendan; Abass, Ahmed

doi:10.1016/j.heliyon.2023.e13944

Cited by 1 publication

(3 citation statements)

References 117 publications

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“…Due to the lack of posterior corneal measurements from the Medmont E300, this surface was modelled using patient-specific clinically measured CCT. Average and standard deviation values were 0.554 ± 0.033 mm with a range 0.472 to 0.628 mm, in line within the reported ranges in the literature [36][37][38][39], and peripheral corneal thickness was taken to be 0.18 mm thicker than the centre [40,41] with the corneal thickness in between being linearly distributed [27]. Cornea models consisted of 16,400 three-dimensional eight-node solid hybrid elements (C3D8H) with 20,646 nodes in five layers based on a mesh convergence preliminary study.…”

Section: Anterior Eye Modelsupporting

confidence: 83%

“…Analyses were carried out in Abaqus FEA software using the implicit integration scheme, and the nonlinear geometry option "NLGEOM" was active. A custom-built nonparametric meshing method was used, originated by Abass et al and subsequently implemented in several previous publications [14,15,26,27]. The geometry coordinates were initially converted into spherical coordinates where azimuths, elevations, and radii were used to cubically interpolate the radii of a uniform pre-meshed sphere or hemisphere, as Once the power difference map of ∆P t was calculated, the CFZ and the ASZ were automatically identified using a technique outlined in [19] which detects pre-and post-wear power profile intersections while avoiding any edge effect [25] by disregarding detected intersections close to the edges of the power map.…”

Section: Finite Element Modellingmentioning

confidence: 99%

“…In order to maintain the best conceivable level of accuracy, the fitted data within the Medmont E300 measured area were replaced by the original measured surface data, leaving the extrapolated points to cover the lost surface points toward the edges only. In order to achieve a balance between building a full eye model that is time consuming to run and a representative anterior eye model with a lower number of nodes and elements, the averaged eye model developed in [27] was used to extend the anterior eye model 2.5 mm beyond the limbal area. This extension ensured that FEA boundary conditions were not affecting the corneal area where the lens was interacting with the eye.…”

Section: Anterior Eye Modelmentioning

confidence: 99%

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FEA-Based Stress–Strain Barometers as Forecasters for Corneal Refractive Power Change in Orthokeratology

Wu,

Lin,

et al. 2024

Bioengineering

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Purpose: To improve the effectivity of patient-specific finite element analysis (FEA) to predict refractive power change (RPC) in rigid Ortho-K contact lens fitting. Novel eyelid boundary detection is introduced to the FEA model to better model the effects of the lid on lens performance, and stress and strain outcomes are investigated to identify the most effective FEA components to use in modelling. Methods: The current study utilises fully anonymised records of 249 eyes, 132 right eyes, and 117 left eyes from subjects aged 14.1 ± 4.0 years on average (range 9 to 38 years), which were selected for secondary analysis processing. A set of custom-built MATLAB codes was built to automate the process from reading Medmont E300 height and distance files to processing and displaying FEA stress and strain outcomes. Measurements from before and after contact lens wear were handled to obtain the corneal surface change in shape and power. Tangential refractive power maps were constructed from which changes in refractive power pre- and post-Ortho-K wear were determined as the refractive power change (RPC). A total of 249 patient-specific FEA with innovative eyelid boundary detection and 3D construction analyses were automatically built and run for every anterior eye and lens combination while the lens was located in its clinically detected position. Maps of four stress components: contact pressure, Mises stress, pressure, and maximum principal stress were created in addition to maximum principal logarithmic strain maps. Stress and strain components were compared to the clinical RPC maps using the two-dimensional (2D) normalised cross-correlation and structural similarity (SSIM) index measure. Results: On the one hand, the maximum principal logarithmic strain recorded the highest moderate 2D cross-correlation area of 8.6 ± 10.3%, and contact pressure recorded the lowest area of 6.6 ± 9%. Mises stress recorded the second highest moderate 2D cross-correlation area with 8.3 ± 10.4%. On the other hand, when the SSIM index was used to compare the areas that were most similar to the clinical RPC, maximum principal stress was the most similar, with an average strong similarity percentage area of 26.5 ± 3.3%, and contact pressure was the least strong similarity area of 10.3 ± 7.3%. Regarding the moderate similarity areas, all components were recorded at around 34.4% similarity area except the contact pressure, which was down to 32.7 ± 5.8%. Conclusions: FEA is an increasingly effective tool in being able to predict the refractive outcome of Ortho-K treatment. Its accuracy depends on identifying which clinical and modelling metrics contribute to the most accurate prediction of RPC with minimal ocular complications. In terms of clinical metrics, age, Intra-ocular pressure (IOP), central corneal thickness (CCT), surface topography, lens decentration and the 3D eyelid effect are all important for effective modelling. In terms of FEA components, maximum principal stress was found to be the best FEA barometer that can be used to predict the performance of Ortho-K lenses. In contrast, contact pressure provided the worst stress performance. In terms of strain, the maximum principal logarithmic strain was an effective strain barometer.

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