Department of Experimental Ophthalmology, Saarland University, /Saar, 66424, Homburg, Germany.
Dr. Rolf M. Schwiete Center for Limbal Stem Cell and Aniridia Research, Saarland University, /Saar, 66424, Homburg, Germany.
Graefes Arch Clin Exp Ophthalmol. 2024 Mar;262(3):835-846. doi: 10.1007/s00417-023-06208-9. Epub 2023 Sep 1.
Intraocular lenses (IOLs) require proper positioning in the eye to provide good imaging performance. This is especially important for premium IOLs. The purpose of this study was to develop prediction models for estimating IOL decentration, tilt and the axial IOL equator position (IOLEQ) based on preoperative biometric and tomographic measures.
Based on a dataset (N = 250) containing preoperative IOLMaster 700 and pre-/postoperative Casia2 measurements from a cataractous population, we implemented shallow feedforward neural networks and multilinear regression models to predict the IOL decentration, tilt and IOLEQ from the preoperative biometric and tomography measures. After identifying the relevant predictors using a stepwise linear regression approach and training of the models (150 training and 50 validation data points), the performance was evaluated using an N = 50 subset of test data.
In general, all models performed well. Prediction of IOL decentration shows the lowest performance, whereas prediction of IOL tilt and especially IOLEQ showed superior performance. According to the 95% confidence intervals, decentration/tilt/IOLEQ could be predicted within 0.3 mm/1.5°/0.3 mm. The neural network performed slightly better compared to the regression, but without significance for decentration and tilt.
Neural network or linear regression-based prediction models for IOL decentration, tilt and axial lens position could be used for modern IOL power calculation schemes dealing with 'real' IOL positions and for indications for premium lenses, for which misplacement is known to induce photic effects and image distortion.
人工晶状体（IOL）需要在眼中正确定位，以提供良好的成像性能。这对于高级 IOL 尤为重要。本研究的目的是基于术前生物测量和断层扫描测量数据，开发预测模型，以估算 IOL 偏心、倾斜和轴向 IOL 赤道位置（IOLEQ）。
基于包含白内障人群的术前 IOLMaster 700 和预/术后 Casia2 测量值的数据集（N=250），我们实施了浅层前馈神经网络和多线性回归模型，以预测术前生物测量和断层扫描测量值的 IOL 偏心、倾斜和 IOLEQ。使用逐步线性回归方法和模型训练（150 个训练和 50 个验证数据点）识别相关预测因子后，使用 N=50 个测试数据子集评估性能。
一般来说，所有模型的性能都很好。IOL 偏心的预测表现最低，而 IOL 倾斜的预测，尤其是 IOLEQ 的预测表现更好。根据 95%置信区间，可在 0.3 毫米/1.5°/0.3 毫米的范围内预测偏心/倾斜/IOLEQ。神经网络的性能略优于回归，但对于偏心和倾斜没有显著差异。
可使用基于神经网络或线性回归的 IOL 偏心、倾斜和轴向晶状体位置预测模型，用于处理“真实”IOL 位置的现代 IOL 功率计算方案，以及用于可能导致光学效应和图像失真的高级镜片的适应证。
