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Article Dans Une Revue Medical Image Analysis Année : 2022

Surgical data science – from concepts toward clinical translation

1 DKFZ - German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg]
2 Universität Heidelberg [Heidelberg] = Heidelberg University
3 Gazi University
4 LTSI - Laboratoire Traitement du Signal et de l'Image
5 IRCAD/EITS - Institut de Recherche Contre les Cancers de l'Appareil Digestif-European Institute of Telesurgery
6 JHU - Johns Hopkins University
7 La société Karl STORZ
8 TUM - Technische Universität Munchen - Technical University Munich - Université Technique de Munich
9 Imperial College London
10 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
11 IHU Strasbourg - L'Institut hospitalo-universitaire de Strasbourg
12 UNIVR - Università degli studi di Verona = University of Verona
13 Johns Hopkins University School of Medicine [Baltimore]
14 Stanford University School of Medicine [CA, USA]
15 UCL - University College of London [London]
16 Sheikh Zayed Institute for Pediatric Surgical Innovation, Washington DC
17 Queen's University [Kingston, Canada]
18 IRIMAS - Institut de Recherche en Informatique Mathématiques Automatique Signal - IRIMAS - UR 7499
19 Monash university
20 University of Toronto
21 Kyushu University
22 Heidelberg University Hospital [Heidelberg]
23 HMS - Harvard Medical School [Boston]
24 UNIBE - Universität Bern / University of Bern
25 Leipzig University / Universität Leipzig
26 University Hospital Leipzig = Universitätsklinikum Leipzig
27 LMU - Ludwig Maximilian University [Munich] = Ludwig Maximilians Universität München
28 Massachusetts General Hospital [Boston]
29 KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
30 BCM - Baylor College of Medicine
31 University Health Network
32 UCL Institute of Neurology, Queen Square [London]
33 TH - Universität Karlsruhe
34 University Hospital Hamburg-Eppendorf
35 NCT - National Center for Tumor Diseases [Dresden]
Kyle Lam
  • Fonction : Auteur

Résumé

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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Origine : Publication financée par une institution

Dates et versions

hal-03515942 , version 1 (09-05-2022)

Identifiants

Citer

Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya, Keno März, Toby Collins, et al.. Surgical data science – from concepts toward clinical translation. Medical Image Analysis, 2022, 76, pp.102306. ⟨10.1016/j.media.2021.102306⟩. ⟨hal-03515942⟩
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