Development of a Neural Network to Predict Optimal IOP Reduction in Glaucoma Management.

Abstract

Glaucoma management relies on lowering intraocular pressure (IOP), but determining the target reduction at presentation is challenging, particularly in normal-tension glaucoma (NTG). We developed and internally validated a neural network regression model using retrospective clinical data from Qiu et al. (2015), including 270 patients (118 with NTG). A single-layer artificial neural network with five nodes was trained in MATLAB R2024b using the Levenberg-Marquardt algorithm. Inputs included demographic, refractive, structural, and functional parameters, with IOP reduction as the output. Data were split into 65% training, 15% validation, and 20% testing, with training repeated 10 times. Model performance was strong and consistent (average RMSE: 1.90 ± 0.29 training, 2.18 ± 0.34 validation, 2.11 ± 0.30 testing; Pearson's r: 0.92 ± 0.02, 0.88 ± 0.02, 0.88 ± 0.04). The best-performing model achieved RMSEs of 1.57, 2.90, and 1.77 with r values of 0.93, 0.91, and 0.93, respectively. Feature ablation revealed significant contributions from IOP, axial length, CCT, diagnosis, VCDR, spherical equivalent, mean deviation, and laterality. This study demonstrates that a simple neural network can reliably predict individualized IOP reduction targets, supporting personalized glaucoma management and improved outcomes.