AUTHOR=Desouky Nada A. , Saafan Mahmoud M. , Mansour Mohamed H. , Maklad Osama M. 

TITLE=Patient-specific air puff-induced loading using machine learning

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=Volume 11 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2023.1277970

DOI=10.3389/fbioe.2023.1277970

ISSN=2296-4185

ABSTRACT=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 was a time-consuming model that is impractical to use in clinical practice and in large parametric studies. Our study provides a supervised machine learning algorithm that predicts the corneal deformation and the time-dependent air puff pressure distribution for different corneal parameters via a parametric study of the gradient boosting algorithm. The results confirmed that the algorithm gives the time-dependent air puff pressure distribution with a MAE of 0.0258, a RMSE of 0.0673, and an execution time of 93 sec, which is then applied to the finite element model of the eye generating the corresponding corneal deformation taking into account the FSI influence with around 99.2% reduction in computational time. 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).