• Murilo Barreto Souza, Fabricio Witzel Medeiros, Danilo Barreto Souza, Renato Garcia, and Milton Ruiz Alves.
• Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil. murilobsouza@gmail.com
• Clinics (Sao Paulo). 2010 Jan 1; 65 (12): 122312281223-8.

PurposeTo evaluate the performance of support vector machine, multi-layer perceptron and radial basis function neural network as auxiliary tools to identify keratoconus from Orbscan II maps.MethodsA total of 318 maps were selected and classified into four categories: normal (n = 172), astigmatism (n = 89), keratoconus (n = 46) and photorefractive keratectomy (n = 11). For each map, 11 attributes were obtained or calculated from data provided by the Orbscan II. Ten-fold cross-validation was used to train and test the classifiers. Besides accuracy, sensitivity and specificity, receiver operating characteristic (ROC) curves for each classifier were generated, and the areas under the curves were calculated.ResultsThe three selected classifiers provided a good performance, and there were no differences between their performances. The area under the ROC curve of the support vector machine, multi-layer perceptron and radial basis function neural network were significantly larger than those for all individual Orbscan II attributes evaluated (p < 0.05).ConclusionOverall, the results suggest that using a support vector machine, multi-layer perceptron classifiers and radial basis function neural network, these classifiers, trained on Orbscan II data, could represent useful techniques for keratoconus detection.

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