PURPOSE: To evaluate an artificial neural network in order to correctly identify Orbscan II TM tests of patients with normal and keratoconus corneas. METHODS: A retrospective analysis included 98 Orbscan II TM tests of 59 subjects and an artificial neural network was created and trained based on the Java Neural Network 1.1 software. Seventy-three tests (59 normal tests and 14 keratoconus examinations) were applied to train the neural network and 25 eyes were used to test the method (19 normal eyes and 6 cases of keratoconus corneas). RESULTS: Backpropagation method was performed to train the neural network to 5% error and 0.2 learning rate. The trained neural network presented sensibility and specificity of 83 and 100% respectively. CONCLUSION: Artificial neural network can accurately help clinicians to classify keratoconus in Orbscan II TM tests.
Keratoconus; Dilatation, pathologic; Cornea; Neural networks (computer); Visual fields
