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J Cataract Refract Surg. 2004 Apr;30(4):863-6.

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

Author information

1
Department of Ophthalmology, Medical University of Vienna, Währinger Strasse 13, 1090 Vienna, Austria.

Abstract

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.

PMID:
15093652
DOI:
10.1016/j.jcrs.2003.08.027
[Indexed for MEDLINE]

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