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.

数据科学，特别是机器学习的最新发展改变了专家们对手术未来的设想。外科数据科学（SDS）是一个新的研究领域，旨在通过数据的捕获、组织、分析和建模来提高介入医疗质量。尽管放射学和临床数据科学领域研究了越来越多的数据驱动方法和临床应用，但外科手术仍然缺乏转化成功案例。在本出版物中，我们阐明了根本原因，并为该领域的未来发展提供了路线图。基于 SDS 领域领先研究人员参加的国际研讨会，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许多主题的可用标准和工具，即 (1) 数据采集、存储的基础设施以及在存在监管限制的情况下的访问，(2) 数据注释和共享，以及 (3) 数据分析。我们进一步补充了这一技术观点，包括 (4) 对当前可用的 SDS 产品和学术界的转化进展进行回顾，以及 (5) 基于国际多轮 Delphi 的更快临床转化和充分利用 SDS 潜力的路线图过程。