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            <funder>This work was supported by the European Research Council (ERC) starting grant COMBIOSCOPY under the New Horizon Framework Programme grant agreement [ERC-2015-StG-637960]; the Helmholtz Imaging Platform (HIP), a platform of the Helmholtz Incubator on Information and Data Science; the NCT Heidelberg; BPI France (project CONDOR); the Johns Hopkins Science of Learning Institute Research Grant; the National Institutes of Health [NIDCR R01 DE025265, P41 EB015902, P41 EB015898, R01 CA235589]; the Surgical Oncology Program of the National Center for Tumor Diseases (NCT) Heidelberg; KARL STORZ SE &amp; Co. KG; the Royal Society (UF140290) and NIHR Imperial BRC (Biomedical Research Centre); the ERC - H2020 Autonomous Robotic Surgery (ARS) grant agreement [ERC-2016-ADG-742671]; the Surgical Metrics Project - American College of Surgeons (National Society Contract); the Quantified Physician - 7-SIGMA Simulation Systems, Minnesota, MN (Industry Contract); the Tourniquet Master Training - DOD SBIR Phase IIb - Continuation Award [W81XWH-13-C-0021]; the Ontology for Human Motion and Psychomotor Performance - Stanford University Media-X; Motion Analysis for Microvascular Anastomosis - University of Wisconsin (Academic Contract); the Precision Learning Initiative - American Medical Association (National Society Grant); Quantifying the Metrics of Surgical Mastery: An Exploration in Data Science (NIH) [R01DK123445]; the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) [203145Z/16/Z]; Engineering and Physical Sciences Research Council (EPSRC) [EP/P027938/1, EP/R004080/1, EP/P012841/1]; the Royal Academy of Engineering Chair in Emerging Technologies; the St. Michael’s Hospital; the University of Toronto; the Grant-in-Aid for Scientific Research on Innovative Area from MEXT, Japan; the National Cancer Data Ecosystem, contract number 19X037Q under Task Order HHSN26100071 from NCI; the project ProteCT [BMBF 16SV8568]; the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany’s Excellence Strategy - EXC 2050/1 - Project ID 390696704 - Cluster of Excellence “Centre for Tactile Internet with Human-in-the-Loop” (CeTI); the Canada Research Chair in Computer Integrated Surgery, Natural Sciences and Engineering Research Council of Canada; the ANR with grants ANR-16-CE33-0009 (project DeepSurg), ANR-10-IAHU-02 (IHU Strasbourg) and ANR-20-CHIA-0029-01 (Chair AI4ORSafety); and the Federal Ministry of Economics and Energy (BMWi) and the German Aerospace Center (DLR) within the OP 4.1 project [BMWI 01MT17001C].</funder>
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                  <date type="datePub">2022-02</date>
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                <term xml:lang="en">Computer aided surgery</term>
                <term xml:lang="en">Deep learning</term>
                <term xml:lang="en">Artificial intelligence</term>
                <term xml:lang="en">Surgical data science</term>
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              <p>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.</p>
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          <orgName type="acronym">TUM</orgName>
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