用扩散模型生成青光眼视野预测的不确定性分布，更贴近临床实际。
Beyond Point Estimates for Glaucoma Visual Field Forecasting with Diffusion Models
- 采用条件去噪扩散模型，从不规则随访数据生成未来视野的分布预测
- 在两个独立队列上验证，预测分布校准度高，关键指标误差低于现有方法
- 适合需要评估疾病风险不确定性的医生和研究者，推动个性化诊疗
青光眼视野（VF）预测对个性化监测和治疗规划至关重要。由于疾病进展异质性和测量变异性，该任务具有固有不确定性，但现有方法多输出单一确定性预测，无法体现不确定性。本文将视野预测建模为概率预测问题，采用条件去噪扩散模型，从纵向观测数据中生成未来视野的可能分布，支持不规则随访间隔。在两个独立的视野队列上的实验表明，基于扩散模型的预测能生成对临床相关指标具有良好校准性的分布。当简化为标准点估计时，该方法在准确率上优于临床基线和先前学习方法。结果凸显分布建模在视野预测中的优势，支持向不确定性感知、临床可解释的风险评估转变。
原文摘要 · Abstract (English)
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.
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