Artificial neural network approaches of aqueous flow and heat transfer in the anterior chamber of the human eye

An Artificial Neural Network (ANN) model has been developed to investigate the flow and thermal characteristics of aqueous humor (AH) within the anterior chamber (AC) of the human eye. The influence of convective heat transfer coefficients (CHTC) and thermal conductivity (TC) on intraocular velocity and temperature distributions plays a critical role in regulating ocular function and preserving overall eye health. An ANN is trained on data derived from modified Navier-Stokes equations formulated under lubrication theory, incorporating convective and no-slip boundary conditions at the corneal surface. The model produced mathematical formulations for profile of temperature, velocity and stream function, providing close relation with analytical expectations. Graphical analysis highlights the ANN's ability to capture variations in AH velocity and temperature distribution with changing TC and CHTC. To ensure the reliability of our results, we validated the ANN model predictions against established experimental data and numerical simulations. Our findings align well with previous simulations, enhancing understanding of ocular fluid mechanics and health implications. Error values are less than 0.1$\%$, demonstrating its potential as a predictive tool for understanding intraocular flow mechanisms.