Analysis of nonlinear systems to estimate intraocular lens position after cataract surgery.
OBJECTIVE
To compare the performance of neural networks with that of linear regression to predict the postoperative effective lens position (ELP) from preoperative biometry measurements.


METHODS
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.


CONCLUSIONS
The prediction of postoperative ACD with the MLP was not significantly better than the prediction using linear regression.
Findl
Oliver
O
Department of Ophthalmology, Medical University of Vienna, Währinger Strasse 13, 1090 Vienna, Austria.
Struhal
Walter
W
Dorffner
Georg
G
Drexler
Wolfgang
W
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000867:Anterior Chamber / Q000033:anatomy & histology*; D001699:Biometry; D006801:Humans; D007368:Interferometry; D019654:Lens Implantation, Intraocular; D007910:Lenses, Intraocular; D008027:Light; D008875:Middle Aged; D016571:Neural Networks, Computer; D018918:Phacoemulsification; D011184:Postoperative Period; D012029:Refraction, Ocular; D014792:Visual Acuity