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Surgical data science – from concepts toward clinical translation

Lena Maier-Hein 1, 2, * Matthias Eisenmann 1 Duygu Sarikaya 3, 4 Keno März 1 Toby Collins 5 Anand Malpani 6 Johannes Fallert 7 Hubertus Feussner 8 Stamatia Giannarou 9 Pietro Mascagni 10, 11 Hirenkumar Nakawala 12 Adrian Park 13 Carla Pugh 14 Danail Stoyanov 15 Swaroop S. Vedula 6 Kevin Cleary 16 Gabor Fichtinger 17 Germain Forestier 18, 19 Bernard Gibaud 4 Teodor Grantcharov 20 Makoto Hashizume 21 Doreen Heckmann-Nötzel 1 Hannes G. Kenngott 22 Ron Kikinis 23 Lars Mündermann 7 Nassir Navab 6, 8 Sinan Onogur 1 Tobias Ross 1 Raphael Sznitman 24 Russell H. Taylor 6 Minu D. Tizabi 1 Martin Wagner 22 Gregory D. Hager 6 Thomas Neumuth 25 Nicolas Padoy 10, 11 Justin Collins 15 Ines Gockel 26 Jan Goedeke 27 Daniel A. Hashimoto 28 Luc Joyeux 29, 30 Kyle Lam 9 Daniel R. Leff 9 Amin Madani 31 Hani J. Marcus 32 Ozanan Meireles 23, 28 Alexander Seitel 1 Dogu Teber 33 Frank Ückert 34 Beat P. Müller-Stich 22 Pierre Jannin 4 Stefanie Speidel 35 
* Corresponding author
Abstract : 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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Submitted on : Monday, May 9, 2022 - 8:27:15 AM
Last modification on : Tuesday, September 27, 2022 - 4:24:49 AM
Long-term archiving on: : Wednesday, August 10, 2022 - 6:30:45 PM


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Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya, Keno März, Toby Collins, et al.. Surgical data science – from concepts toward clinical translation. Medical Image Analysis, Elsevier, 2022, 76, pp.102306. ⟨10.1016/⟩. ⟨hal-03515942⟩



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