The use of different cardiac imaging modalities such as MRI, CT or ultrasound enables the visualization and interpretation of altered morphological structures and function of the heart. In recent years, there has been an increasing interest in AI and deep learning that take into account spatial and temporal information in medical image analysis. In particular, deep learning tools using temporal information in image processing have not yet found their way into daily clinical practice, despite its presumed high diagnostic and prognostic value. This review aims to synthesize the most relevant deep learning methods and discuss their clinical usability in dynamic cardiac imaging using for example the complete spatiotemporal image information of the heart cycle. Selected articles were categorized according to the following indicators: clinical applications, quality of datasets, preprocessing and annotation, learning methods and training strategy, and test performance. Clinical usability was evaluated based on these criteria by classifying the selected papers into (i) clinical level, (ii) robust candidate and (iii) proof of concept applications. Interestingly, not a single one of the reviewed papers was classified as a “clinical level” study. Almost 39% of the articles achieved a “robust candidate” and as many as 61% a “proof of concept” status. In summary, deep learning in spatiotemporal cardiac imaging is still strongly research-oriented and its implementation in clinical application still requires considerable efforts. Challenges that need to be addressed are the quality of datasets together with clinical verification and validation of the performance achieved by the used method.

使用不同的心脏成像方式（例如MRI，CT或超声）可以可视化和解释心脏的形态结构和功能。近年来，人们越来越关注在医学图像分析中考虑到时空信息的AI和深度学习。尤其是，尽管在图像处理中使用时间信息的深度学习工具被认为具有很高的诊断和预后价值，但它们尚未进入日常临床实践。这篇综述旨在综合最相关的深度学习方法，并使用例如心动周期的完整时空图像信息来讨论其在动态心脏成像中的临床可用性。选定的文章根据以下指标进行分类：临床应用，数据集质量，预处理和注释，学习方法和培训策略以及测试性能。基于这些标准，通过将所选论文分为（i）临床水平，（ii）可靠的候选者和（iii）概念验证应用，评估了临床可用性。有趣的是，没有一篇综述文章被归类为“临床水平”研究。几乎39％的文章获得了“稳健的候选人”，多达61％的文章获得了“概念证明”状态。总而言之，时空心脏成像中的深度学习仍然强烈地以研究为导向，并且其在临床应用中的实施仍然需要大量的努力。