The growing interest in machine learning (ML) in healthcare is driven by the promise of improved patient care. However, how many ML algorithms are currently being used in clinical practice? While the technology is present, as demonstrated in a variety of commercial products, clinical integration is hampered by a lack of infrastructure, processes, and tools. In particular, automating the selection of relevant series for a particular algorithm remains challenging. In this work, we propose a methodology to automate the identification of brain MRI sequences so that we can automatically route the relevant inputs for further image-related algorithms. The method relies on metadata required by the Digital Imaging and Communications in Medicine (DICOM) standard, resulting in generalizability and high efficiency (less than 0.4 ms/series). To support our claims, we test our approach on two large brain MRI datasets (40,000 studies in total) from two different institutions on two different continents. We demonstrate high levels of accuracy (ranging from 97.4 to 99.96%) and generalizability across the institutions. Given the complexity and variability of brain MRI protocols, we are confident that similar techniques could be applied to other forms of radiological imaging.

中文翻译：

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使用 DICOM 元数据进行放射影像系列分类：大型临床脑 MRI 数据集的可行性研究。

医疗保健领域对机器学习 (ML) 日益增长的兴趣是由改善患者护理的承诺推动的。但是，目前有多少 ML 算法用于临床实践？虽然该技术存在，正如在各种商业产品中所证明的那样，但由于缺乏基础设施、流程和工具，临床整合受到阻碍。特别是，为特定算法自动选择相关系列仍然具有挑战性。在这项工作中，我们提出了一种自动识别大脑 MRI 序列的方法，以便我们可以自动路由相关输入以用于进一步的图像相关算法。该方法依赖于医学数字成像和通信 (DICOM) 标准所需的元数据，具有通用性和高效率（小于 0.4 ms/系列）。为了支持我们的主张，我们在来自两个不同大陆的两个不同机构的两个大型大脑 MRI 数据集（总共 40,000 项研究）上测试了我们的方法。我们展示了高水平的准确性（从 97.4% 到 99.96%）和跨机构的普遍性。鉴于脑 MRI 协议的复杂性和可变性，我们相信类似的技术可以应用于其他形式的放射成像。