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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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Background: The aim is to investigate induced higher-order aberrations (HOA)s and astigmatism as a result of non-toric ortho-k lens decentration and utilise artificial intelligence (AI) to predict its magnitude and direction. Methods: Medmont E300 Video topographer was used to scan 249 corneas before and after ortho-k wear. Custom-built MATLAB codes extracted topography data and determined lens decentration from the boundary and midpoint of the central flattened treatment zone (TZ). An evaluation was carried out by conducting Zernike polynomial fittings via a computer-coded digital signal processing procedure. Finally, an AI-based machine learning neural network algorithm was developed to predict the direction and magnitude of TZ decentration. Results: Analysis of the first 21 Zernike polynomial coefficients indicate that the four low-order and four higher-order aberration terms were changed significantly by ortho-k wear. While baseline astigmatism was not correlated with lens decentration (R = 0.09), post-ortho-k astigmatism was moderately correlated with decentration (R = 0.38) and the difference in astigmatism (R = 0.3). Decentration was classified into three groups: ≤0.50 mm, reduced astigmatism by -0.9 ± 1 D; 0.5~1 mm, increased astigmatism by 0.8 ± 0.1 D; >1 mm, increased astigmatism by 2.7 ± 1.6 D and over 50% of lenses were decentred >0.5 mm. For lenses decentred >1 mm, 29.8% of right and 42.7% of left lenses decentred temporal-inferiorly and 13.7% of right and 9.4% of left lenses decentred temporal-superiorly. AI-based prediction successfully identified the decentration direction with accuracies of 70.2% for right and 71.8% for left lenses and predicted the magnitude of decentration with root-mean-square (RMS) of 0.31 mm and 0.25 mm for right and left eyes, respectively. Conclusions: Ortho-k lens decentration is common when fitting non-toric ortho-k lenses, resulting in induced HOAs and astigmatism, with the magnitude being related to the amount of decentration. AI-based algorithms can effectively predict decentration, potentially allowing for better control over ortho-k fitting and, thus, preferred clinical outcomes.
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Purpose: To investigate the relationship between Ortho-K contact lens design parameters and refractive power change of the eye through a parametric mathematical representation. Methods: The current study utilises fully anonymized records of 249 eyes, 132 right eyes, and 117 left eyes from subjects aged 14.1 ± 4.0 years on average (range 9-38 years) which were selected for secondary analysis processing. The data were split into 3 groups (G1 up to 35 days wear, from 10 to 35 days, G2 up to 99 days wear, more than 35-99 days & G3 more than 100 days wear) according to the length of time, in days, that the lenses were worn. Corneal shape was measured before and after contact lens wear using the Medmont E300 topographer, from which height and distance files were read by a custom-built MATLAB code to construct the corneal anterior surface independently. Changes in refractive power pre and post-Ortho-K wear were determined using constructed tangential refractive power maps from which both centrally flattened and annular steepened zones were automatically bounded, hence used to determine the refractive power change. Results: On average, flat Sim-K and steep Sim-K were reduced after Ortho-K lens wear by 1.6 ± 1.3 D and 1.3 ± 1.4 D respectively. The radius of the base curve was correlated with the mean central flattened zone power change strongly in G1 (R = 0.7, p < 0.001) and moderately in G2 (R = 0.4) and G3 (R = 0.4, p < 0.001). Hence, a strong correlation with the base curve was recorded in group G1 and moderate in G2 and G3. The reverse curve was very strongly correlated to the mean central flattened zone power change in G1 (R = 0.8, p < 0.001) and strongly correlated with G2 (R = 0.6, p < 0.001) and G3 (R = 0.7, p < 0.001). The reverse curve was also strongly correlated with the mean annular steepened zone power change among all groups G1, G2, and G3 (R = 0.7, R = 0.6 and R = 0.6) respectively (p < 0.001). Conclusions: Although the central corneal refractive power change was strongly correlated to the Ortho-K lens base curve, it characterized only 50% of the target power change. However, the annular steepened zone refractive power change appears to be a clearer predictor of target power change, as there appears to be a one-to-one inverse relationship with the target refractive power correction. Differences between these results and the literature may be a result of the topography software smoothing effect.