Demuth S, Paris J, Faddeenkov I, De Sèze J, Gourraud P-A
Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France; Inserm U1119 : biopathologie de la myéline, neuroprotection et stratégies thérapeutiques, University of Strasbourg, 1, rue Eugène-Boeckel - CS 60026, 67084 Strasbourg, France.
Inserm U1064, CR2TI - Center for Research in Transplantation and Translational Immunology, Nantes University, 44000 Nantes, France.
Rev Neurol (Paris). 2025 Mar;181(3):135-155. doi: 10.1016/j.neurol.2024.04.004. Epub 2024 May 20.
Deep learning (DL) is an artificial intelligence technology that has aroused much excitement for predictive medicine due to its ability to process raw data modalities such as images, text, and time series of signals.
Here, we intend to give the clinical reader elements to understand this technology, taking neuroinflammatory diseases as an illustrative use case of clinical translation efforts. We reviewed the scope of this rapidly evolving field to get quantitative insights about which clinical applications concentrate the efforts and which data modalities are most commonly used.
We queried the PubMed database for articles reporting DL algorithms for clinical applications in neuroinflammatory diseases and the radiology.healthairegister.com website for commercial algorithms.
The review included 148 articles published between 2018 and 2024 and five commercial algorithms. The clinical applications could be grouped as computer-aided diagnosis, individual prognosis, functional assessment, the segmentation of radiological structures, and the optimization of data acquisition. Our review highlighted important discrepancies in efforts. The segmentation of radiological structures and computer-aided diagnosis currently concentrate most efforts with an overrepresentation of imaging. Various model architectures have addressed different applications, relatively low volume of data, and diverse data modalities. We report the high-level technical characteristics of the algorithms and synthesize narratively the clinical applications. Predictive performances and some common a priori on this topic are finally discussed.
The currently reported efforts position DL as an information processing technology, enhancing existing modalities of paraclinical investigations and bringing perspectives to make innovative ones actionable for healthcare.
深度学习（DL）是一种人工智能技术，因其能够处理图像、文本和信号时间序列等原始数据形式，在预测医学领域引发了广泛关注。
在此，我们旨在为临床读者提供理解该技术的要素，以神经炎症性疾病作为临床转化努力的一个示例性用例。我们回顾了这个快速发展领域的范围，以获得关于哪些临床应用集中了研究努力以及最常使用哪些数据形式的定量见解。
我们在PubMed数据库中查询了报告用于神经炎症性疾病临床应用的深度学习算法的文章，并在radiology.healthairegister.com网站上查询了商业算法。
该综述纳入了2018年至2024年间发表的148篇文章和5种商业算法。临床应用可分为计算机辅助诊断、个体预后、功能评估、放射学结构分割以及数据采集优化。我们的综述突出了研究努力方面的重要差异。放射学结构分割和计算机辅助诊断目前集中了大部分研究努力，且成像方面的研究占比过高。各种模型架构已应用于不同的应用、相对较少的数据量以及多样的数据形式。我们报告了算法的高级技术特征，并对临床应用进行了叙述性综合。最后讨论了该主题的预测性能和一些常见的先验知识。
目前报告的研究努力将深度学习定位为一种信息处理技术，增强了现有的临床辅助检查方式，并为使创新方式在医疗保健中可行带来了前景。
