Analysis of nonlinear systems to estimate intraocular lens position after cataract surgery

Purpose: To compare the performance of neural networks with that of linear regression to predict the postoperative effective lens position (ELP) from preoperative biometry measurements. Setting: Departments of Ophthalmology, Medical Cybernetics and Artificial Intelligence, and Medical Physics, Medical University of Vienna, Vienna, Austria. Methods: The neural-network-type multilayer perceptron (MLP) and a linear regression technique were used to predict ELP. Suitable MLP models and variable input combinations were selected by extended-feature subset selection. Apart from the usual preoperative biometric variables, anterior chamber depth and lens thickness were measured with partial coherence interferometry and white-to-white measurements were used as input variables. Results: Prediction of ELP could be improved from a correlation coefficient (Pearson) of 0.54 for linear regression to a coefficient of 0.68 for the MLP; however, this difference was not statistically significant. Conclusion: The prediction of postoperative ACD with the MLP was not significantly better than the prediction using linear regression.