Anterior Segment and Refractive Surgery Department, Rothschild Foundation Hospital, Paris, France.
Anterior Segment and Refractive Surgery Department, Rothschild Foundation Hospital, Paris, France.
Cont Lens Anterior Eye. 2023 Dec;46(6):102063. doi: 10.1016/j.clae.2023.102063. Epub 2023 Sep 28.
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.
To develop a deep learning model to predict the base curve (R0) of rigid gas permeable contact lenses for keratoconus patients.
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.
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.
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.
硬性透气性角膜接触镜（RGP）是为圆锥角膜患者提供最佳视力的最有效手段。对于眼科医生和患者来说，RGP 的适配可能具有挑战性且耗时。深度学习预测模型可以简化这个过程。
开发一种深度学习模型，以预测圆锥角膜患者硬性透气性角膜接触镜的基弧（R0）。
我们在 2012 年 6 月至 2021 年 4 月期间在 Rothschild 基金会医院进行了一项回顾性研究。我们纳入了所有使用 Menicon Rose K2®镜片适配的圆锥角膜患者。数据分为训练集以开发模型和测试集以评估模型的性能。我们使用 U 型网络架构。从每位患者的 Scheimpflug 检查中提取毫米为单位的前轴曲率原始矩阵，并将其作为模型的输入。计算预测值与规定的 R0 之间的平均绝对误差（MAE）。进行单变量和多变量分析以评估模型的误差。
纳入 202 名患者的 358 只眼：287 只眼纳入训练数据集，71 只眼纳入测试数据集。我们的模型的 Pearson 决定系数（R2）为 0.83，而制造商推荐值（平均角膜曲率，Km）为 0.75。我们的模型的均方误差为 0.04，而 Km 为 0.11。预测的 R0 MAE（0.16±0.13）与 Km MAE（0.23±0.23）有统计学显著差异（p=0.02）。在多变量分析中，顶点中心位于中央 5mm 区域之外是唯一显著增加预测绝对误差的因素。
与制造商的建议相比，我们的深度学习方法在预测圆锥角膜患者硬性透气性角膜接触镜基弧方面表现出更高的精度。这种方法在复杂的适配病例中可能特别有益，并可以帮助减少眼科医生和患者在这个过程中花费的时间。
