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 a 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.

深度学习在医学成像自动诊断方面取得了突破，并在眼科领域取得了许多成功的应用。然而，标准医学图像分类方法仅评估采集时的疾病存在，忽略了纵向成像的常见临床设置。对于年龄相关性黄斑变性 (AMD) 和原发性开角型青光眼 (POAG) 等缓慢进展的眼部疾病，患者会随着时间的推移进行重复成像以跟踪疾病进展，预测未来发生疾病的风险对于正确计划治疗至关重要。我们提出的用于生存分析的纵向变压器 (LTSA) 能够通过纵向医学成像进行动态疾病预测，根据在长时间不规则时间段内捕获的眼底摄影图像序列对疾病发生时间进行建模。使用来自年龄相关眼病研究 (AREDS) 和高眼压治疗研究 (OHTS) 的纵向成像数据，在晚期 AMD 预后和 18/20 的 19/20 头对头比较中，LTSA 显着优于单图像基线POAG 预后的比较。时间注意力分析还表明，虽然最近的图像通常最具影响力，但先前的成像仍然提供额外的预后价值。