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

J Cataract Refract Surg. 2004 Apr;30(4):863-6. doi: 10.1016/j.jcrs.2003.08.027.


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.

Publication types

  • Comparative Study

MeSH terms

  • Adult
  • Aged
  • Aged, 80 and over
  • Anterior Chamber / anatomy & histology*
  • Biometry
  • Humans
  • Interferometry
  • Lens Implantation, Intraocular
  • Lenses, Intraocular*
  • Light
  • Middle Aged
  • Neural Networks, Computer*
  • Phacoemulsification
  • Postoperative Period
  • Refraction, Ocular
  • Visual Acuity