Neural networks and statistical analysis for classification of corneal videokeratography maps based on Zernike coefficients: a quantitative comparison.
OBJECTIVE
The main goal of this study was to develop and compare two different techniques for classification of specific types of corneal shapes when Zernike coefficients are used as inputs. A feed-forward artificial Neural Network (NN) and discriminant analysis (DA) techniques were used.


METHODS
The inputs both for the NN and DA were the first 15 standard Zernike coefficients for 80 previously classified corneal elevation data files from an Eyesys System 2000 Videokeratograph (VK), installed at the Departamento de Oftalmologia of the Escola Paulista de Medicina, São Paulo. The NN had 5 output neurons which were associated with 5 typical corneal shapes: keratoconus, with-the-rule astigmatism, against-the-rule astigmatism, "regular" or "normal" shape and post-PRK.


RESULTS
The NN and DA responses were statistically analyzed in terms of precision ([true positive+true negative]/total number of cases). Mean overall results for all cases for the NN and DA techniques were, respectively, 94% and 84.8%.


CONCLUSIONS
Although we used a relatively small database, results obtained in the present study indicate that Zernike polynomials as descriptors of corneal shape may be a reliable parameter as input data for diagnostic automation of VK maps, using either NN or DA.
Vieira de Carvalho
Luis Alberto
LA
Instituto de Física de São Carlos, Universidade de São Paulo, São Carlos, SP, Brasil. lavcf@if.sc.usp.br
Barbosa
Marconi Soares
MS
D000465:Algorithms; D001251:Astigmatism / Q000473:pathology; D003315:Cornea / Q000473:pathology / Q000601:surgery; D019781:Corneal Topography / Q000379:methods*; D016002:Discriminant Analysis; D006801:Humans; D007640:Keratoconus / Q000473:pathology; D016571:Neural Networks, Computer; D018903:Photorefractive Keratectomy