Mathematical modelling of eye condition in glaucoma: Approaches to parameter analysis and their interactions

Mathematical modelling of physiological processes is a key component of intelligent medical systems, as it describes disease mechanisms in greater detail and contributes to early diagnosis. This study presents an analytical model for assessing eye health, incorporating key ophthalmological parameters: intraocular pressure (IOP), perfusion coefficient (Pperf), best-corrected visual acuity (BCVA), visual field index (VFI), retinal nerve fibre layer thickness (RNFL), and neuroretinal rim area (Rim_area). The study aimed to develop a model that can accurately evaluate the nonlinear interactions between these parameters, improving diagnostic accuracy and predicting glaucoma progression. The study also aimed to determine critical threshold values of these ophthalmological indicators to improve clinical decision-making. The results demonstrated that application of numerical optimisation techniques such as L-BFGS-B and logarithmic-exponential transformations significantly improves the accuracy of glaucoma risk prediction; critical threshold values of ophthalmological parameters have been identified, improving precision of detection of glaucoma stages. Additionally, the study facilitates a systematic evaluation of the association between intraocular pressure and optic nerve condition, a factor deemed critical for accurate prediction of disease progression. The practical significance of this research is determined by the potential integration into medical IT systems for automated glaucoma screening and patient monitoring. The proposed approach can assist ophthalmologists in clinical decision-making by optimising treatment strategies and preventing irreversible vision loss. The model’s adaptability also enables its use in telemedicine applications, facilitating remote diagnostics and continuous patient assessment