Remtulla et al. investigate the use of an artificial neural network to predict the optimal intraocular pressure (IOP) reduction needed for individualized glaucoma management. Remtulla et al. developed and tested a single-layer artificial neural network using retrospective clinical data from patients with glaucoma. They trained, validated, and tested the model on different data subsets to predict the amount of intraocular pressure reduction needed based on various patient characteristics. Remtulla et al. found that the neural network accurately predicted the optimal intraocular pressure reduction, showing strong correlations between predicted and actual values. Furthermore, analysis also revealed that baseline IOP, axial length, central corneal thickness, glaucoma subtype, and optic nerve measurements were the most influential factors in determining the required pressure reduction. Overall, Remtulla et al. emphasized that the neural network model could support personalized glaucoma treatment by accurately predicting patient-specific pressure targets, although further validation with larger and more diverse datasets is needed before clinical application.