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Glaucoma is an eye condition characterized by increased intraocular pressure that damages the retina and optic nerve, leading to irreversible blindness if left untreated. The human eye has various components, including the cornea, iris, pupil, lens, and optic nerve. Aqueous humor is secreted by the epithelium of the ciliary body in the posterior chamber and flows through the trabecular meshwork and canal of Schlemm, maintaining normal intraocular pressure. The trabecular meshwork and the canal...
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In open-angle glaucoma, the iridocorneal angle remains open, but the trabecular meshwork becomes stiff, slowing down the outflow of aqueous humor. This causes a buildup of aqueous humor in the anterior chamber, leading to a sudden increase in intraocular pressure. The treatment for open-angle glaucoma focuses on reducing the elevated intraocular pressure by either decreasing the secretion of aqueous humor or increasing its outflow.
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Development of a Neural Network to Predict Optimal IOP Reduction in Glaucoma Management.

Raheem Remtulla1, Sidrat Rahman2, Hady Saheb1,2,3

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|October 24, 2025
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A neural network model can predict personalized intraocular pressure (IOP) reduction targets for glaucoma patients, including normal-tension glaucoma (NTG). This supports individualized treatment and better patient outcomes.

Keywords:
feature importanceglaucoma managementneural networkprecision ophthalmology

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Area of Science:

  • Ophthalmology
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Glaucoma management necessitates lowering intraocular pressure (IOP), but precise target reductions are difficult to ascertain, especially for normal-tension glaucoma (NTG).
  • Personalized treatment strategies are crucial for optimizing glaucoma care and preventing disease progression.

Purpose of the Study:

  • To develop and validate a neural network model for predicting individualized IOP reduction targets in glaucoma patients.
  • To assess the model's performance and identify key predictive features for personalized glaucoma management.

Main Methods:

  • A single-layer artificial neural network was trained using retrospective clinical data from 270 patients (118 with NTG).
  • Input parameters included demographic, refractive, structural, and functional data; the output was IOP reduction.
  • Data were split into training (65%), validation (15%), and testing (20%) sets, with the model trained 10 times.

Main Results:

  • The neural network demonstrated strong and consistent performance across training, validation, and testing datasets (average RMSE ~2.0-2.2, Pearson's r ~0.88-0.92).
  • The best model achieved RMSEs of 1.57-2.90 and r values of 0.91-0.93.
  • Feature ablation identified significant contributions from IOP, axial length, central corneal thickness (CCT), diagnosis, visual field index, spherical equivalent, mean deviation, and laterality.

Conclusions:

  • A simple neural network can reliably predict individualized IOP reduction targets for glaucoma management.
  • This predictive capability supports personalized treatment strategies, potentially leading to improved patient outcomes in glaucoma care.