作者
Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya, Keno März, Toby Collins, Anand Malpani, Johannes Fallert, Hubertus Feussner, Stamatia Giannarou, Pietro Mascagni, Hirenkumar Nakawala, Adrian Park, Carla Pugh, Danail Stoyanov, Swaroop S Vedula, Kevin Cleary, Gabor Fichtinger, Germain Forestier, Bernard Gibaud, Teodor Grantcharov, Makoto Hashizume, Doreen Heckmann-Nötzel, Hannes G Kenngott, Ron Kikinis, Lars Mündermann, Nassir Navab, Sinan Onogur, Tobias Roß, Raphael Sznitman, Russell H Taylor, Minu D Tizabi, Martin Wagner, Gregory D Hager, Thomas Neumuth, Nicolas Padoy, Justin Collins, Ines Gockel, Jan Goedeke, Daniel A Hashimoto, Luc Joyeux, Kyle Lam, Daniel R Leff, Amin Madani, Hani J Marcus, Ozanan Meireles, Alexander Seitel, Dogu Teber, Frank Ückert, Beat P Müller-Stich, Pierre Jannin, Stefanie Speidel
发表日期
2022/2/1
来源
Medical image analysis
卷号
76
页码范围
102306
出版商
Elsevier
简介
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 …
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