The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstructured or high-dimensional data such as images, text or omics data. In this work, we conduct a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related attributes. In summary, the reviewed methods often address only a small subset of tasks relevant to time-to-event data - e.g., single-risk right-censored data - and neglect to incorporate more complex settings. Our findings are summarized in an editable, open-source, interactive table: https://survival-org.github.io/DL4Survival. As this research area is advancing rapidly, we encourage community contribution in order to keep this database up to date.

翻译：近年来，深度学习技术在生存分析领域的引入推动了方法论的显著进展，例如从图像、文本或组学数据等非结构化或高维数据中学习的能力。本文对基于深度学习的事件时间分析方法进行了系统性综述，并根据生存分析与深度学习相关属性对其特征进行归纳。研究表明，综述的方法通常仅针对事件时间数据中的少量任务（例如单风险右删失数据），而未能纳入更复杂的场景。我们的发现总结于一个可编辑、开源、交互式表格中，网址为：https://survival-org.github.io/DL4Survival。鉴于该研究领域发展迅速，我们鼓励社区参与以持续更新此数据库。