Daily disposable soft lens sagittal depth and other lens parameter changes measured at room temperature (20 °C) and on-eye temperature (34 °C)

PURPOSE

To investigate the differences in contact lens sagittal depth (CL-SAG), base curve (BC), and overall lens diameter (OAD) of daily disposable (DD) SCLs at room temperature (20 °C) and on-eye (34 °C) temperature.

METHODS

Twelve different spherical daily SCLs were analyzed (labeled power -3.00 D) at 20 °C and 34 °C. The posterior CL-SAG, BC, and OAD were measured directly using a spectral domain OCT-based instrument (Optimec is830).

RESULTS

The CL-SAG range for the daily disposable lenses measured at 20 °C was 3468-4016 µm. At 34 °C, the DD CL-SAG of all lenses individually decreased compared to 20 °C (t = 7.78, p < 0.001), ranging from 3385 to 3913 µm. On average at the higher temperature, the measured overall lens diameter was smaller (t = 8.18, p < 0.001) and the BC was marginally steeper (t = 2.11, p = 0.058).

CONCLUSIONS

Soft lens parameters change when equilibrated from room temperature (20 °C) to on-eye temperature (34 °C). All lenses shrink when going to on-eye temperature (both lens diameter and CL-SAG decreases), compared to lenses measured at room temperature by ISO standards. Higher water content materials tend to have greater changes to the lens parameters compared to lower water content material lenses.

Novel deep learning approach to estimate rigid gas permeable contact lens base curve for keratoconus fitting

INTRODUCTION

Rigid gas permeable contact lenses (RGP) are the most efficient means of providing optimal vision in keratoconus. RGP fitting can be challenging and time-consuming for ophthalmologists and patients. Deep learning predictive models could simplify this process.

OBJECTIVE

To develop a deep learning model to predict the base curve (R0) of rigid gas permeable contact lenses for keratoconus patients.

METHODS

We conducted a retrospective study at the Rothschild Foundation Hospital between June 2012 and April 2021. We included all keratoconus patients fitted with Menicon Rose K2® lenses. The data was divided into a training set to develop the model and a test set to evaluate the model's performance. We used a U-net architecture. The raw matrix of anterior axial curvature in millimeters was extracted from Scheimpflug examinations for each patient and used as input for the model. The mean absolute error (MAE) between the prediction and the prescribed R0 was calculated. Univariate and multivariate analyses were conducted to assess the model's errors.

RESULTS

Three hundred fifty-eight eyes from 202 patients were included: 287 eyes were included in the training dataset, and 71 were included in the testing dataset. Our model's Pearson coefficient of determination (R2) was calculated at 0.83, compared to 0.75 for the manufacturer's recommendation (mean keratometry, Km). The mean square error of our model was calculated at 0.04, compared to 0.11 for Km. The predicted R0 MAE (0.16 ± 0.13) was statistically significantly different from the Km MAE (0.23 ± 0.23) (p = 0.02). In multivariate analysis, an apex center outside the central 5 mm region was the only factor significantly increasing the prediction absolute error.

CONCLUSION

Our deep learning approach demonstrated superior precision in predicting rigid gas permeable contact lens base curves for keratoconus patients compared to the manufacturer's recommendation. This approach has the potential to be particularly beneficial in complex fitting cases and can help reduce the time spent by ophthalmologists and patients during the process.

Predicting initial base curve of the rigid contact lenses according to Javal keratometry findings in patients with keratoconus

PURPOSE

To find an appropriate correlation between the base curve (BC) of rigid gas permeable (RGP) contact lenses and manual keratometry findings in Iranian patients with keratoconus (KCN) in order to simplify the fitting process, reduce the time, and lower the costs.

METHOD

This retrospective study was done in 121 eyes of 69 patients with KCN fitted with a specific trial set of RGP contact lenses over a 7-year period. The specifications of the final lens parameters included power, total diameter (1), and BC, the first two of which were fixed in all of the lenses in the trial set while BC was changed in 0.1-mm steps. Javal keratometer was used to measure keratometric values. The final fit assessment was performed based on the standard criterion of "three-point touch".

RESULT

Single and multiple linear correlations were done and the result was the following equation: BC = 2.455 (constant of the final multiple regression model) + 0.280 (steep keratometry) + 0.368 (flat keratometry) + 0.047 (corneal astigmatism) CONCLUSION: The advantages of this study include simplifying the RGP fitting process, reducing the examination time, lowering the costs, enhancing the confidence of the examiners and patients, easier lens fitting in remote places where more advanced devices are not accessible.