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.