Beyond Point Estimates for Glaucoma Visual Field Forecasting with Diffusion Models
For glaucoma clinicians, it provides uncertainty-aware predictions that could improve personalized monitoring and treatment planning, addressing a key limitation of deterministic methods.
The paper tackles probabilistic forecasting of visual fields in glaucoma using diffusion models, achieving state-of-the-art point-estimate accuracy and well-calibrated uncertainty distributions on two independent cohorts.
Forecasting visual fields (VFs) is critical for personalized monitoring and treatment planning in glaucoma. This is inherently uncertain due to heterogeneous disease progression and measurement variability, yet most existing methods produce single deterministic predictions that fail to represent this uncertainty. We formulate VF forecasting as a probabilistic prediction problem and the use of conditioned denoising diffusion models to generate distributions of plausible future VFs from longitudinal observations with irregular follow-up intervals. Experiments on two independent VF cohorts show that diffusion-based predictions produce well-calibrated distributions for clinically relevant VF measures. When reduced to a standard point-estimate, the proposed approach achieves state-of-the-art accuracy compared to clinical baselines and prior learning-based methods. Our results highlight the advantages of distributional modeling for VF forecasting and support a shift from point-estimate prediction toward uncertainty-aware, clinically interpretable risk assessment in glaucoma.