Seiler Moritz, Ritter Kerstin
Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin, Berlin, Germany.
Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.
Pflugers Arch. 2025 Apr;477(4):571-589. doi: 10.1007/s00424-024-03016-w. Epub 2024 Oct 8.
Recently, deep generative modelling has become an increasingly powerful tool with seminal work in a myriad of disciplines. This powerful modelling approach is supposed to not only have the potential to solve current problems in the medical field but also to enable personalised precision medicine and revolutionise healthcare through applications such as digital twins of patients. Here, the core concepts of generative modelling and popular modelling approaches are first introduced to consider the potential based on methodological concepts for the generation of synthetic data and the ability to learn a representation of observed data. These potentials will be reviewed using current applications in neuroimaging for data synthesis and disease decomposition in Alzheimer's disease and multiple sclerosis. Finally, challenges for further research and applications will be discussed, including computational and data requirements, model evaluation, and potential privacy risks.
最近，深度生成建模已成为一种越来越强大的工具，在众多学科中都有开创性的工作。这种强大的建模方法不仅有望解决医学领域当前的问题，还能通过患者数字孪生等应用实现个性化精准医疗并彻底改变医疗保健。在此，首先介绍生成建模的核心概念和流行的建模方法，以便基于用于生成合成数据的方法概念和学习观测数据表示的能力来考虑其潜力。将使用神经成像在阿尔茨海默病和多发性硬化症的数据合成与疾病分解方面的当前应用来回顾这些潜力。最后，将讨论进一步研究和应用面临的挑战，包括计算和数据要求、模型评估以及潜在的隐私风险。
