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 impact of convective heat transfer coefficients (CHTC) and thermal conductivity (TC) on intraocular fluid 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 using a 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 a 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 the understanding of ocular fluid mechanics and health implications.