Surgical data science - from concepts toward clinical translation.
Surgical data science - from concepts toward clinical translation.
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DOI：
10.1016/j.media.2021.102306
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发表时间：
2022-03
影响因子：
10.9
通讯作者：
Speidel, Stefanie
中科院分区：
文献类型：
--
作者：
Maier-Hein, Lena;Eisenmann, Matthias;Sarikaya, Duygu;Maerz, Keno;Collins, Toby;Malpani, Anand;Fallert, Johannes;Feussner, Hubertus;Giannarou, Stamatia;Mascagni, Pietro;Nakawala, Hirenkumar;Park, Adrian;Pugh, Carla;Stoyanov, Danail;Vedula, Swaroop S.;Cleary, Kevin;Fichtinger, Gabor;Forestier, Germain;Gibaud, Bernard;Grantcharov, Teodor;Hashizume, Makoto;Heckmann-Noetzel, Doreen;Kenngott, Hannes G.;Kikinis, Ron;Muendermann, Lars;Navab, Nassir;Onogur, Sinan;Ross, Tobias;Sznitman, Raphael;Taylor, Russell H.;Tizabi, Minu D.;Wagner, Martin;Hager, Gregory D.;Neumuth, Thomas;Padoy, Nicolas;Collins, Justin;Gockel, Ines;Goedeke, Jan;Hashimoto, Daniel A.;Joyeux, Luc;Lam, Kyle;Leff, Daniel R.;Madani, Amin;Marcus, Hani J.;Meireles, Ozanan;Seitel, Alexander;Teber, Dogu;Ueckert, Frank;Mueller-Stich, Beat P.;Jannin, Pierre;Speidel, Stefanie
关键词：
数据科学的最新发展，特别是机器学习，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的质量。虽然在放射学和临床数据科学领域已经研究了越来越多的数据驱动方法和临床应用，但在外科手术中仍然缺乏转化的成功案例。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一些主题的可用标准和工具，即（1）在监管限制下的数据采集，存储和访问基础设施，（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，（4）审查目前可用的SDS产品和学术界的翻译进展，（5）基于国际多轮德尔菲过程，加快临床翻译和开发SDS的全部潜力的路线图。
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.
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影响因子：
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作者：
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影响因子：
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通讯作者：
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