Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developing late-AMD, the most severe form of AMD. Our study hypothesizes that 1) incorporating historical data improves predictive strength of developing late-AMD and 2) state-of-the-art deep-learning techniques extract more predictive image features than clinicians do. We incorporate longitudinal data from the Age-Related Eye Disease Studies and deep-learning extracted image features in survival settings to predict development of late-AMD. To extract image features, we used multi-task learning frameworks to train convolutional neural networks. Our findings show 1) incorporating longitudinal data improves prediction of late-AMD for clinical standard features, but only the current visit is informative when using complex features and 2) “deep-features” are more informative than clinician derived features. We make codes publicly available at https://github.com/bionlplab/AMD_prognosis_amia2021.

中文翻译：

---

使用AREDS纵向数据对晚期AMD预后进行基于多任务深度学习的生存分析

年龄相关性黄斑变性 (AMD) 是视力丧失的主要原因。一些患者在延迟的时间范围内经历视力丧失，而另一些患者则以快速的速度丧失。医生分析访问时间眼底照片以预测患者发生晚期 AMD（最严重的 AMD 形式）的风险。我们的研究假设 1) 结合历史数据提高了发展晚期 AMD 的预测强度，2) 最先进的深度学习技术比临床医生提取更多的预测图像特征。我们将年龄相关眼病研究的纵向数据和深度学习提取的生存环境中的图像特征结合起来，以预测晚期 AMD 的发展。为了提取图像特征，我们使用多任务学习框架来训练卷积神经网络。我们的研究结果表明 1) 结合纵向数据提高了对临床标准特征的晚期 AMD 的预测，但在使用复杂特征时，只有当前就诊才能提供信息，2)“深层特征”比临床医生衍生的特征提供更多信息。我们在 https://github.com/bionlplab/AMD_prognosis_amia2021 上公开提供代码。