Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany.
Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany.
Med Image Anal. 2022 Feb;76:102306. doi: 10.1016/j.media.2021.102306. Epub 2021 Nov 18.
Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.
近年来，数据科学的发展，特别是机器学习的发展，改变了专家对手术未来的设想。外科数据科学（SDS）是一个新的研究领域，旨在通过捕获、组织、分析和建模数据来提高介入性医疗保健的质量。虽然越来越多的数据驱动方法和临床应用已经在放射学和临床数据科学领域得到了研究，但手术领域仍然缺乏转化成功的案例。在本出版物中，我们揭示了背后的原因，并为该领域的未来发展提供了路线图。基于一个涉及 SDS 领域领先研究人员的国际研讨会，我们回顾了当前的实践、主要成就和举措，以及与该领域相关的多个主题的可用标准和工具，即（1）在存在监管限制的情况下的数据获取、存储和访问的基础设施，（2）数据注释和共享，以及（3）数据分析。我们进一步通过（4）对目前可用的 SDS 产品和学术领域的转化进展进行审查，以及（5）基于国际多轮 Delphi 过程的更快的临床转化和充分利用 SDS 潜力的路线图，来补充这一技术视角。
