[Detection of keratoconus based on a neural network with Orbscan].
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.
Souza
Murilo Barreto
MB
IMédico Docente da Disciplina de Informática Médica do Curso de Medicina da Faculdade de Tecnologia e Ciências - Salvador (BA) - Brasil. murilobsouza@gmail.com
Medeiros
Fabrício Witzel de
FW
Souza
Danilo Barreto
DB
Alves
Milton Ruiz
MR
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