Developing and Evaluating Deep Learning Approaches for Visual Field Denoising in Glaucoma
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Purpose
To investigate the relative efficacy of nine distinct visual field (VF) denoising artificial intelligence (AI) methods and a pathology-aware AI strategy to discourage over-correction of glaucomatous defects.
Design
Retrospective study
Participants
87,940 paired visual field (VF) and optical coherence tomography (OCT) samples from a tertiary academic center.
Methods
Denoising models were trained on a separate VF-only dataset and evaluated on an independent structure-function dataset of paired VF-OCT samples. We implemented and evaluated nine distinct VF denoising strategies representing three broad categories: baseline measurements, self-supervised and image restoration models (including Noise2Noise, Noise2Void, and NAFNet), and latent variable compression-based models (autoencoders and variational autoencoders). All models were designed to reconstruct VF sensitivity maps. We then predicted retinal nerve fiber layer thickness (RNFLT) maps from the denoised VFs using a fixed, independently trained VF-to-RNFLT prediction model.
Main Outcome Measures
Predicted VF and RNFLT maps and resultant evaluation metrics.
Results
The raw VF baseline achieved a global R² of 0.5468 and MAE of 16.83 μm. Restoration-based models maintained or slightly improved concordance, with the pathology-aware NAFNet achieving the highest global R² (0.5485) and a comparable MAE (16.82 μm). In contrast, compression-based models degraded concordance, with CNN-VAE showing a significant reduction (R² ≈ 0.50).
In severe glaucoma, concordance decreased across all methods; however, compression architectures exhibited disproportionately greater degradation compared with restoration-based approaches.
Conclusions
We present a comparative benchmark of AI-based VF denoising strategies paired with structure–function evaluation. While restoration-based models can reduce variability without loss of biological signal, latent compression risks attenuating clinically meaningful defects. Visually smoother fields are not necessarily more biologically accurate.
