Surgical data science – from concepts toward clinical translation

Research output: Contribution to journalReview articleContributedpeer-review

Contributors

  • Lena Maier-Hein - , German Cancer Research Center (DKFZ), Heidelberg University  (Author)
  • Matthias Eisenmann - , German Cancer Research Center (DKFZ) (Author)
  • Duygu Sarikaya - , Gazi University, Université de Rennes 1 (Author)
  • Keno März - , German Cancer Research Center (DKFZ) (Author)
  • Toby Collins - , University of Strasbourg (Author)
  • Anand Malpani - , Johns Hopkins University (Author)
  • Johannes Fallert - , Karl Storz SE & Co. KG (Author)
  • Hubertus Feussner - , Technical University of Munich (Author)
  • Stamatia Giannarou - , Imperial College London (Author)
  • Pietro Mascagni - , University of CNRS, Institute of Image-Guided Surgery (Author)
  • Hirenkumar Nakawala - , University of Verona (Author)
  • Adrian Park - , Anne Arundel Health System, Johns Hopkins University (Author)
  • Carla Pugh - , Stanford University (Author)
  • Danail Stoyanov - , University College London (Author)
  • Swaroop S. Vedula - , Johns Hopkins University (Author)
  • Kevin Cleary - , Children's National Medical Center (Author)
  • Gabor Fichtinger - , Queen's University Kingston (Author)
  • Germain Forestier - , University of Upper Alsace, Monash University (Author)
  • Bernard Gibaud - , Université de Rennes 1 (Author)
  • Teodor Grantcharov - , University of Toronto (Author)
  • Makoto Hashizume - , Kyushu University, Kitakyushu Koga Hospital (Author)
  • Doreen Heckmann-Nötzel - , German Cancer Research Center (DKFZ) (Author)
  • Hannes G. Kenngott - , Heidelberg University  (Author)
  • Ron Kikinis - , Harvard University (Author)
  • Lars Mündermann - , Karl Storz SE & Co. KG (Author)
  • Nassir Navab - , Technical University of Munich, Johns Hopkins University (Author)
  • Sinan Onogur - , German Cancer Research Center (DKFZ) (Author)
  • Tobias Roß - , German Cancer Research Center (DKFZ), Heidelberg University  (Author)
  • Raphael Sznitman - , University of Bern (Author)
  • Russell H. Taylor - , Johns Hopkins University (Author)
  • Minu D. Tizabi - , German Cancer Research Center (DKFZ) (Author)
  • Martin Wagner - , University Hospital Heidelberg (Author)
  • Gregory D. Hager - , Johns Hopkins University (Author)
  • Thomas Neumuth - , Leipzig University (Author)
  • Nicolas Padoy - , University of CNRS, Institute of Image-Guided Surgery (Author)
  • Justin Collins - , University College London (Author)
  • Ines Gockel - , Leipzig University (Author)
  • Jan Goedeke - , Ludwig Maximilian University of Munich (Author)
  • Daniel A. Hashimoto - , Case Western Reserve University, Harvard University (Author)
  • Luc Joyeux - , KU Leuven, Texas Children's Hospital Houston (Author)
  • Kyle Lam - , Imperial College London (Author)
  • Daniel R. Leff - , Imperial College London, Imperial College Healthcare NHS Trust (Author)
  • Amin Madani - , University of Toronto (Author)
  • Hani J. Marcus - , University College London (Author)
  • Ozanan Meireles - , Harvard University (Author)
  • Alexander Seitel - , German Cancer Research Center (DKFZ) (Author)
  • Dogu Teber - , City Hospital Karlsruhe (Author)
  • Frank Ückert - , University of Hamburg (Author)
  • Beat P. Müller-Stich - , Heidelberg University  (Author)
  • Pierre Jannin - , Université de Rennes 1 (Author)
  • Stefanie Speidel - , National Center for Tumor Diseases (Partners: UKD, MFD, HZDR, DKFZ), Clusters of Excellence CeTI: Centre for Tactile Internet (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.

Details

Original languageEnglish
Article number102306
Number of pages46
JournalMedical Image Analysis
Volume76
Publication statusPublished - Feb 2022
Peer-reviewedYes

External IDs

Scopus 85120910690
PubMed 34879287

Keywords

Keywords

  • Artificial intelligence, Clinical translation, Computer aided surgery, Deep learning, Surgical data science