title: [Detection of keratoconus based on a neural network with Orbscan].
abstract: OBJECTIVE
  To evaluate an artificial neural network in order to correctly identify Orbscan II tests of patients with normal and keratoconus corneas.
  
  
  METHODS
  A retrospective analysis included 98 Orbscan II 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.
  
  
  CONCLUSIONS
  Artificial neural network can accurately help clinicians to classify keratoconus in Orbscan II tests.
authors.affiliation: IMédico Docente da Disciplina de Informática Médica do Curso de Medicina da Faculdade de Tecnologia e Ciências - Salvador (BA) - Brasil. murilobsouza@gmail.com
authors.forename: Murilo Barreto
authors.initials: MB
authors.lastname: Souza
authors.forename: Fabrício Witzel de
authors.initials: FW
authors.lastname: Medeiros
authors.forename: Danilo Barreto
authors.initials: DB
authors.lastname: Souza
authors.forename: Milton Ruiz
authors.initials: MR
authors.lastname: Alves
mesh_terms: D016022:Case-Control Studies; D019781:Corneal Topography / Q000379:methods*; D006801:Humans; D007640:Keratoconus / Q000175:diagnosis*; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012680:Sensitivity and Specificity