Using Artificial Neural Networks to Identify Glaucoma Stages

Glaucoma is one of the principal causes of blindness in the world1. It is an illness which has an asymptomatic form until advanced stages, thus early diagnosis represents an important objective to achieve with the aim that people who present Glaucoma maintain the best visual acuity throughout life, thereby improving their quality of life. An Artificial Neural Network (ANN) is proposed for the diagnosis of Glaucoma. Automated combination and analysis of information from structural and functional diagnostic techniques were performed to improve Glaucoma detection in the clinic. In our work we contribute the inclusion of Artificial Intelligence and neuronal networks in the diverse systems of clinical exploration and autoperimetry and laser polarimetry, with the objective of facilitating the adequate staging in a rapid and automatic way and thus to be able to act in the most adequate manner possible. Data from clinical examination, standard perimetry and analysis of the nerve fibers of the retina with scanning laser polarimetry (NFAII;GDx) were integrated in a system of Artificial Intelligence. Different tools in the diagnosis of Glaucoma by an automatic classification system were explained based on ANN. In the present work an analysis of 106 eyes, in accordance with the stage of glaucomatous illness was used to develop an ANN. Multilayer perceptron was provided with the Levenberg-Marquardt method. The learning was carried out with half of the data and with the training function of gradient descent w/momentum backpropagation and was checked by the diagnosis of a Glaucoma expert ophthalmologist. A correct classification of each eye in the corresponding stage of Glaucoma has been achieved. Specificity and sensitivity are 100%. This method provides an efficient and accurate tool for the diagnosis of Glaucoma in the stages of glaucomatous illness by means of AI techniques.

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