Identification of Glaucoma Stages with Artificial Neural Networks Using Retinal Nerve Fibre Layer Analysis and Visual Field Parameters

摘要

For the diagnosis of glaucoma, we propose a system of Artificial Intelligence that employs Artificial Neural Networks (ANN) and integrates the analysis of the nerve fibres of the retina from the study with scanning laser polarimetry (NFAII;GDx), perimetry and clinical data. The present work shows an analysis of 106 eyes of 53 patients, in accordance with the stage of glaucomatous illness in which each eye was found. The groups defined include stage 0, which corresponds to normal eyes; stage 1, for ocular hypertension; 2, for early glaucoma; 3, for established glaucoma; 4, for advanced glaucoma and 5, for terminal glaucoma. The developed ANN is a multilayer perceptron 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. The other half of the data served to evaluate the model of the neuronal network. A 100% 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.