Patient-specific air puff-induced loading using machine learning

Introduction: The air puff test is a contactless tonometry test used to measure the intraocular pressure and the cornea’s biomechanical properties. Limitations that most challenge the accuracy of the estimation of the corneal material and the intraocular pressure are the strong intercorrelation between the intraocular pressure and the corneal parameters, either the material properties that can change from one person to another because of age or the geometry parameters like central corneal thickness. This influence produces inaccuracies in the corneal deformation parameters while extracting the IOP parametric equation, which can be reduced through the consideration of the patient-specific air puff pressure distribution taking into account the changes in corneal parameters. This air puff pressure loading distribution can be determined precisely from the fluid-structure interaction (FSI) coupling between the air puff and the eye model. However, the computational fluid dynamics simulation of the air puff in the coupling algorithm is a time-consuming model that is impractical to use in clinical practice and large parametric studies. Methods: By using a supervised machine learning algorithm, we predict the time-dependent air puff pressure distribution for different corneal parameters via a parametric study of the corneal deformations and the gradient boosting algorithm. Results: The results confirmed that the algorithm gives the time-dependent air puff pressure distribution with an MAE of 0.0258, an RMSE of 0.0673, and an execution time of 93 s, which is then applied to the finite element model of the eye generating the corresponding corneal deformations taking into account the FSI influence. Using corneal deformations, the response parameters can be extracted and used to produce more accurate algorithms of the intraocular pressure and corneal material stress-strain index (SSI). Discussion: Estimating the distribution of air pressure on the cornea is essential to increase the accuracy of intraocular pressure (IOP) measurements, which serve as valuable indicator of corneal disease. We find that the air puff pressure loading is largely influenced by complex changes in corneal parameters unique to each patient case. With our innovative algorithm, we can preserve the same accuracy developed by the CFD-based FSI model, while reducing the computational time from approximately 101000 s (28 h) to 720 s (12 min), which is about 99.2% reduction in time. This huge improvement in computational cost will lead to significant improvement in the parametric equations for IOP and the Stress-Strain Index (SSI).