Validation of a Neural Network Architecture for Approximating an Analytical Model of Eye Condition

Abstract

This paper presents an intelligent deep learning-based model for comprehensive analysis of the human eye's condition. The developed neural network system integrates key ophthalmological parameters, including intraocular pressure, volumetric blood circulation, visual acuity, visual field index, and perfusion pressure, along with additional factors such as age, vascular health, and genetic predisposition. A synthetic dataset of 250,000 samples was generated based on clinically observed parameter ranges from the Filatov Institute of Eye Diseases and Tissue Therapy. This controlled dataset enabled architectural validation of a neural network model designed to approximate a physiologically meaningful function (Seye). Although real patient data were not used, the study demonstrates the feasibility of building a robust diagnostic framework, laying the groundwork for future application to clinical datasets. The neural network architecture includes three hidden layers with ReLU activation, ensuring high prediction accuracy. Model evaluation demonstrated a high coefficient of determination and low values of root mean squared error and mean absolute percentage error, indicating a strong correlation between predicted and actual values. The obtained results confirm the potential of neural network methods for automated eye condition analysis. The proposed model can be applied for early diagnosis and monitoring of ophthalmological diseases, as well as a decision-support tool in clinical practice. Future work includes integrating real medical data to enhance the model's generalizability and developing hybrid approaches that combine traditional mathematical methods with deep learning. © 2025 Copyright for this paper by its authors.