Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally we summarised the most recent developments. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work was on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly presented in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. Summarised, deep learning will change positively the workflow of interventional radiotherapy but there is room for improvement when it comes to reproducible results and standardised evaluation methods.
翻译:在所有医疗领域,特别是在医学成像领域,它起着很大的作用。然而,在干预性放射治疗(布拉希姆疗法)中,深层学习仍处于早期阶段。在本次审查中,首先,我们调查并审视了在所有干预性放射治疗过程和直接相关领域深层学习的作用。此外,我们总结了最近的发展情况。复制深层学习算法的结果,包括源代码和培训数据,必须提供。因此,这项工作的第二个重点是分析开放源、开放数据和开放模型的可用性。在我们的分析中,我们得以表明深层学习在一些干预性放射治疗领域已经发挥了重要作用,但在另一些领域却很少介绍。然而,随着这些年来,其影响越来越大,部分是自我推动的,但也受到密切相关领域的影响。开放源、数据和模型的数量不断增加,但在不同研究群体中分布仍然稀少和不均匀。出版代码、数据和模型的不情愿限制了对开放源码、开放数据和开放模型的可获取性,将评价限制于单一机构数据集的可用性。在我们的分析中,深层次的学习过程将带来深刻的改进。