Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma

Purpose: Correcting retinal nerve fiber layer thickness (RNFLT) artifacts in glaucoma with deep learning and evaluate its clinical usefulness. Methods: We included 24,257 patients with optical coherence tomography and reliable visual field (VF) measurements within 30 days and 3,233 patients with reliable VF series of at least five measurements over >= 4 years. The artifacts are defined as RNFLT less than the known floor value of 50 mu m. We selected 27,319 high-quality RNFLT maps with an artifact ratio (AR) of 2% as the ground truth. We created pseudo-artifacts from 21,722 low-quality RNFLT maps with AR of 5% and superimposed them on high-quality RNFLT maps to predict the artifact-free ground truth. We evaluated the impact of artifact correction on the structure-function relationship and progression forecasting. Results: The mean absolute error and Pearson correlation of the artifact correction were 9.89 mu m and 0.90 (P 0.001), respectively. Artifact correction improved R2 for VF predic-tion in RNFLT maps with AR of 0.05) the AUC for progression prediction in RNFLT maps with AR of <= 10%, 10%, and >20%: (1) total deviation pointwise progression: 0.68 to 0.69, 0.62 to 0.63, and 0.62 to 0.64; and (2) mean deviation fast progression: 0.67 to 0.68, 0.54 to 0.60, and 0.45 to 0.56. Conclusions: Artifact correction for RNFLTs improves VF and progression prediction in glaucoma. Translational Relevance: Our model improves clinical usability of RNFLT maps with artifacts.