ABSTRACT
BACKGROUND AND OBJECTIVE: This paper builds different neural network models with simple topologies, having one or two hidden layers which were subsequently employed in the prediction of ocular changes progression in patients with diabetes associated with primer open-angle glaucoma. MATERIAL AND METHODS: For attempting to indicate whether there is a relationship between glaucoma and diabetes, a simulation method, based on artificial neural networks (ANN), Jordan Elman networks (JEN) type, in particular, was applied in conjunction with clinical observation. The study was conducted on a sample of 101 eyes with open angle glaucoma included and, in each case, the patients had associated diabetes mellitus. A high degree of accuracy was exhibited by the models, demonstrating the potential effectiveness of this artificial intelligence technique for predicting ocular changes associated with diabetes. The parameters considered in this study for modelling purpose were: glaucoma age, diabetes age, C/D ratio (cup/disk size), glycated haemoglobin level (HbA1c), intraocular pressure (IOP), patient age, mean deviation (MD) and LENS appearance. RESULTS: Relatively simple models, feed-forward neural networks with one or two intermediate layers, provided clinically meaningful data in direct modelling, the probability of correct answers being of 95%. Inverse modelling was also performed, in which MD depreciation was the output parameter. High accuracy was exhibited, in this case, with Jordan Elman networks, with the confidence interval of ±15%. CONCLUSIONS: The neural models have demonstrated the possibility of their use in successfully predicting the relationship between glaucoma and diabetes in a real clinical environment.
