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Sandra Belalcazar, Hernan Andres Rios, Vanessa Carpio, Oscar Julian Perdomo, Claudia Rosa Carvajal, Fabio A Gonzalez, Henning Müller; Convolutional Neural Networks for identification and classification of optic nerve damage features. Invest. Ophthalmol. Vis. Sci. 2018;59(9):1719.
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© ARVO (1962-2015); The Authors (2016-present)
Optic nerve damage is one of the main features for identification of Glaucoma. The convolutional neural networks are a promising tool for early detection of several diseases using medical images, with great importance in developing countries, where access to medical services is difficult. The purpose of the study was to determine the accuracy of convolutional neural networks to identify early optic nerve damage features on optic nerve photographs as a method for automatic detection of Glaucoma suspects
A cross-sectional diagnostic test study was made. A designed model based on convolutional neural networks was used to analyze optic nerve photographs and automatically segment the optic disc, measuring the total optic disc diameter, the thinnest ring radius and the ratio between these two parameters to estimate the optic nerve damage
The experimental results shown that the proposed model is able to calculate the total diameter, the thinnest ring radius and the ratio with a mean absolute percentage error of 9.9%, 23.9% and 24.8% respectively
Convolutional neural networks show good performance in detection of early optic nerve damage features. This study is the first step to build a telemedicine tool to support physicians to detect glaucoma suspects using optic nerve photographs
This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.
Boxplot of model predictions (Blue = Convolutional Neural Network) versus measure taken by expert (Green = Ophthalmologist) about optic nerve total diameter
Boxplot of model predictions (Blue = Convolutional Neural Network) versus measure taken by expert (Green = Ophthalmologist) about optic nerve thinnest ring radius
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