Souza Murilo Barreto, Medeiros Fabrício Witzel de, Souza Danilo Barreto, Alves Milton Ruiz
IMédico Docente da Disciplina de Informática Médica do Curso de Medicina da Faculdade de Tecnologia e Ciências - Salvador (BA) - Brasil.
Arq Bras Oftalmol. 2008 Nov-Dec;71(6 Suppl):65-8. doi: 10.1590/s0004-27492008000700013.
To evaluate an artificial neural network in order to correctly identify Orbscan II tests of patients with normal and keratoconus corneas.
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).
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.
Artificial neural network can accurately help clinicians to classify keratoconus in Orbscan II tests.
评估人工神经网络，以正确识别正常角膜和圆锥角膜患者的Orbscan II检查结果。
一项回顾性分析纳入了59名受试者的98次Orbscan II检查，并基于Java神经网络1.1软件创建并训练了一个人工神经网络。73次检查（59次正常检查和14次圆锥角膜检查）用于训练神经网络，25只眼用于测试该方法（19只正常眼和6例圆锥角膜）。
采用反向传播方法将神经网络训练至误差为5%、学习率为0.2。训练后的神经网络敏感性和特异性分别为83%和100%。
人工神经网络可在Orbscan II检查中准确帮助临床医生对圆锥角膜进行分类。
Zotero 插件