Deep learning has enabled breakthroughs in automated diagnosis from medical imaging, with many successful applications in ophthalmology. However, standard medical image classification approaches only assess disease presence at the time of acquisition, neglecting the common clinical setting of longitudinal imaging. For slow, progressive eye diseases like age-related macular degeneration (AMD) and primary open-angle glaucoma (POAG), patients undergo repeated imaging over time to track disease progression and forecasting the future risk of developing disease is critical to properly plan treatment. Our proposed Longitudinal Transformer for Survival Analysis (LTSA) enables dynamic disease prognosis from longitudinal medical imaging, modeling the time to disease from sequences of fundus photography images captured over long, irregular time periods. Using longitudinal imaging data from the Age-Related Eye Disease Study (AREDS) and Ocular Hypertension Treatment Study (OHTS), LTSA significantly outperformed a single-image baseline in 19/20 head-to-head comparisons on late AMD prognosis and 18/20 comparisons on POAG prognosis. A temporal attention analysis also suggested that, while the most recent image is typically the most influential, prior imaging still provides additional prognostic value.

翻译：深度学习已推动医学影像自动诊断领域取得突破性进展，尤其在眼科领域涌现出大量成功应用。然而，标准的医学影像分类方法仅能评估采集时刻的疾病存在状态，忽视了纵向影像在临床实践中的常规应用场景。针对年龄相关性黄斑变性（AMD）和原发性开角型青光眼（POAG）等进展缓慢的眼部疾病，患者需要通过重复影像检查追踪病程进展，而预测未来患病风险对制定恰当治疗方案至关重要。我们提出的纵向Transformer生存分析模型（LTSA）能够基于纵向医学影像实现动态疾病预后预测，通过对长期不规则时间序列眼底彩照影像的时序建模，预测疾病发生时间。基于年龄相关性眼病研究（AREDS）和眼高压治疗研究（OHTS）的纵向影像数据，LTSA在晚期AMD预后的20组头对头比较中显著优于单次影像基线模型达19组，在POAG预后方面20组比较中占优18组。时间注意力分析还表明，虽然最新影像通常最具影响力，但既往影像仍能提供额外的预后价值。