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Biomechanically Informed Patient-Specific in Silico Models for Laser Refractive Surgery.

Benedetta Fantaci1, Elena Redaelli2,3, Mònica Martí4

  • 1Aragón Institute of Engineering Research (I3A), Universidad de Zaragoza, Zaragoza, Calle de Mariano Esquillor Gómez s/n, 50018, Spain. bfantaci@unizar.es.

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Summary
This summary is machine-generated.

This study introduces a personalized method to simulate refractive surgery outcomes using patient-specific corneal biomechanics. The approach accurately predicts results, enhancing surgical planning for safer, more predictable vision correction.

Keywords:
Artificial neural networkCorneal behaviorCorneal mechanical propertiesFinite element methodNon-contact tonometryRefractive surgery

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Area of Science:

  • Ophthalmology
  • Biomechanical Engineering
  • Computational Modeling

Background:

  • Corneal biomechanics is critical for refractive surgery success.
  • Accurate simulation of surgical effects is needed for personalized treatment planning.

Purpose of the Study:

  • To develop and validate a patient-specific methodology for simulating refractive surgery.
  • To analyze the optomechanical effects of PRK, LASIK, and SMILE.

Main Methods:

  • Incorporated patient-specific corneal biomechanical properties into finite element simulations.
  • Utilized artificial neural networks to estimate mechanical properties from tonometry data.
  • Integrated corneal deformation, intraocular pressure (IOP), and geometry data.

Main Results:

  • The framework successfully simulated postoperative corneal responses with a mean dioptric error of +0.40 ± 0.30 D.
  • SMILE demonstrated the most significant mechanical impact on the corneal model.
  • Patient-specific geometry, IOP, and biomechanics were key inputs.

Conclusions:

  • Introduced a personalized, biomechanically informed approach for simulating corneal behavior post-refractive surgery.
  • The framework improves surgical planning and prediction of refractive stability.
  • Offers a step towards personalized refractive correction with safer, predictable outcomes.