Surgical data science – from concepts toward clinical translation
Maier-Hein L, Eisenmann M, Sarikaya D, Maerz K, Collins T, Malpani A, Fallert J, Feussner H, Giannarou S, Mascagni P, Nakawala H, Park A, Pugh C, Stoyanov D, Vedula SS, Cleary K, Fichtinger G, Forestier G, Gibaud B, Grantcharov T, Hashizume M, Heckmann-Noetzel D, Kenngott HG, Kikinis R, Muendermann L, Navab N, Onogur S, Ross T, Sznitman R, Taylor RH, Tizabi MD, Wagner M, Hager GD, Neumuth T, Padoy N, Collins J, Gockel I, Goedeke J, Hashimoto DA, Joyeux L, Lam K, Leff DR, Madani A, Marcus HJ, Meireles O, Seitel A, Teber D, Ueckert F, Mueller-Stich BP, Jannin P, Speidel S (2022)
Publication Type: Journal article, Review article
Publication year: 2022
Journal
Book Volume: 76
Article Number: 102306
DOI: 10.1016/j.media.2021.102306
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.
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Anne Arundel Medical Center (AAMC)
United States (USA) (US)
Université de Rennes 1 / University of Rennes 1
France (FR)
Deutsches Krebsforschungszentrum (DKFZ)
Germany (DE)
Universitätsklinikum Heidelberg
Germany (DE)
Brigham and Women's Hospital (BWH)
United States (USA) (US)
KARL STORZ SE & Co. KG
Germany (DE)
Universität Bern
Switzerland (CH)
Johns Hopkins University (JHU)
United States (USA) (US)
Universität Leipzig
Germany (DE)
Université de Strasbourg (UDS)
France (FR)
University College London (UCL)
United Kingdom (GB)
Universitätsklinikum Leipzig
Germany (DE)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
Harvard University
United States (USA) (US)
Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven
Belgium (BE)
University Health Network (UHN)
Canada (CA)
University College London Hospitals (UCLH)
United Kingdom (GB)
Städtisches Klinikum Karlsruhe
Germany (DE)
Universitätsklinikum Hamburg-Eppendorf (UKE)
Germany (DE)
Nationales Centrum für Tumorerkrankungen Dresden (NCT/UCC)
Germany (DE)
Gazi University
Turkey (TR)
Research Institute against Digestive Cancer / Institut de Recherche contre les Cancers de l’Appareil Digestif (IRCAD)
France (FR)
Technische Universität München (TUM)
Germany (DE)
University of Verona / Università degli Studi di Verona
Italy (IT)
Stanford University
United States (USA) (US)
Children’s National Health System
United States (USA) (US)
Queen's University
United Kingdom (GB)
Université de Haute-Alsace (UHA)
France (FR)
University of Toronto
Canada (CA)
Kyushu University / 九州大学
Japan (JP)
United Kingdom (GB)
Anne Arundel Medical Center (AAMC)
United States (USA) (US)
Université de Rennes 1 / University of Rennes 1
France (FR)
Deutsches Krebsforschungszentrum (DKFZ)
Germany (DE)
Universitätsklinikum Heidelberg
Germany (DE)
Brigham and Women's Hospital (BWH)
United States (USA) (US)
KARL STORZ SE & Co. KG
Germany (DE)
Universität Bern
Switzerland (CH)
Johns Hopkins University (JHU)
United States (USA) (US)
Universität Leipzig
Germany (DE)
Université de Strasbourg (UDS)
France (FR)
University College London (UCL)
United Kingdom (GB)
Universitätsklinikum Leipzig
Germany (DE)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
Harvard University
United States (USA) (US)
Katholieke Universiteit Leuven (KUL) / Catholic University of Leuven
Belgium (BE)
University Health Network (UHN)
Canada (CA)
University College London Hospitals (UCLH)
United Kingdom (GB)
Städtisches Klinikum Karlsruhe
Germany (DE)
Universitätsklinikum Hamburg-Eppendorf (UKE)
Germany (DE)
Nationales Centrum für Tumorerkrankungen Dresden (NCT/UCC)
Germany (DE)
Gazi University
Turkey (TR)
Research Institute against Digestive Cancer / Institut de Recherche contre les Cancers de l’Appareil Digestif (IRCAD)
France (FR)
Technische Universität München (TUM)
Germany (DE)
University of Verona / Università degli Studi di Verona
Italy (IT)
Stanford University
United States (USA) (US)
Children’s National Health System
United States (USA) (US)
Queen's University
United Kingdom (GB)
Université de Haute-Alsace (UHA)
France (FR)
University of Toronto
Canada (CA)
Kyushu University / 九州大学
Japan (JP)
How to cite
APA:
Maier-Hein, L., Eisenmann, M., Sarikaya, D., Maerz, K., Collins, T., Malpani, A.,... Speidel, S. (2022). Surgical data science – from concepts toward clinical translation. Medical Image Analysis, 76. https://doi.org/10.1016/j.media.2021.102306
MLA:
Maier-Hein, Lena, et al. "Surgical data science – from concepts toward clinical translation." Medical Image Analysis 76 (2022).
BibTeX: Download