Improving the Structure-Function Relationship in Glaucomatous Visual Fields by Using a Deep Learning-Based Noise Reduction Approach

Purpose: To investigate whether processing visual field (VF) measurements using a variational autoencoder (VAE) improves the structure-function relationship in glaucoma. Design: Cross-sectional study. Participants: The training data consisted of 82 433 VF measurements from 16 836 eyes. The testing dataset consisted of 117 eyes of 75 patients with open-angle glaucoma. Methods: A VAE model to reconstruct the threshold of VF was developed using the training dataset. OCT and VF (Humphrey Field Analyzer 24-2, Swedish interactive threshold algorithm standard) measurements were carried out for all eyes in the testing dataset. Visual fields in the testing dataset then were reconstructed using the trained VAE. The structure-function relationship between the circumpapillary retinal nerve fiber layer (cpRNFL) thickness and VF sensitivity was investigated in each of twelve 30 degrees segments of the optic disc (3 nasal sectors were merged). Similarly, the structure-function relationship was investigated using the VAE-reconstructed VF. Main Outcome Measures: Structure-function relationship. Results: The corrected Akaike information criterion values with threshold were found to be smaller than the threshold reconstructed with the VAE (threshold(VAE)) in 9 of 10 sectors. A significant relationship was found between threshold and cpRNFL thickness in 6 of 10 sectors, whereas it was significant in 9 of 10 sectors with threshold(VAE). Conclusions: Applying VAE to VF data results in an improved structure-function relationship. (C) 2020 by the American Academy of Ophthalmology