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Patient-specific air puff-induced loading using machine learning.

Nada A Desouky1, Mahmoud M Saafan2, Mohamed H Mansour1

  • 1Mechanical Power Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.

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|November 29, 2023
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Summary
This summary is machine-generated.

This study introduces a machine learning algorithm to accurately predict air puff pressure distribution for intraocular pressure (IOP) measurements. The method significantly reduces computation time, improving diagnostic accuracy for corneal diseases.

Keywords:
Gradient Boosting Regressor (GBR)air puff pressurefluid-structure interaction (FSI)intraocular pressure (IOP)machine learning (ML)ocular biomechanicsreduced order modelling

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Computational Fluid Dynamics
  • Machine Learning

Background:

  • The air puff test is a contactless method for measuring intraocular pressure (IOP) and corneal biomechanical properties.
  • Inaccuracies in IOP estimation arise from the correlation between IOP and corneal parameters (material and geometry).
  • Current fluid-structure interaction (FSI) models are computationally intensive, limiting clinical application.

Purpose of the Study:

  • To develop a machine learning algorithm for predicting patient-specific air puff pressure distribution.
  • To reduce the computational time of FSI simulations for air puff tests.
  • To improve the accuracy of intraocular pressure (IOP) and corneal material stress-strain index (SSI) measurements.

Main Methods:

  • A supervised machine learning algorithm, specifically gradient boosting, was employed.
  • The algorithm was trained on a parametric study of corneal deformations and air puff pressure distribution.
  • The predicted pressure distribution was applied to a finite element eye model incorporating FSI.

Main Results:

  • The machine learning algorithm accurately predicted time-dependent air puff pressure distribution (MAE: 0.0258, RMSE: 0.0673).
  • Computational time was drastically reduced from ~28 hours to 12 minutes (99.2% reduction).
  • The model generated corneal deformations considering FSI, enabling extraction of response parameters for improved IOP and SSI algorithms.

Conclusions:

  • Accurate estimation of air puff pressure distribution is crucial for precise IOP measurements and corneal disease detection.
  • The developed ML algorithm preserves the accuracy of CFD-based FSI models while significantly reducing computational cost.
  • This advancement facilitates more accurate parametric equations for IOP and SSI, benefiting clinical practice and research.