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| Verfasst von: | Maier-Hein, Lena [VerfasserIn]  |
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| | Eisenmann, Matthias [VerfasserIn] |
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| | Sarikaya, Duygu [VerfasserIn] |
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| | März, Keno [VerfasserIn] |
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| | Collins, Toby [VerfasserIn] |
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| | Malpani, Anand [VerfasserIn] |
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| | Fallert, Johannes [VerfasserIn] |
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| | Feussner, Hubertus [VerfasserIn] |
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| | Giannarou, Stamatia [VerfasserIn] |
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| | Mascagni, Pietro [VerfasserIn] |
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| | Nakawala, Hirenkumar [VerfasserIn] |
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| | Park, Adrian [VerfasserIn] |
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| | Pugh, Carla [VerfasserIn] |
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| | Stoyanov, Danail [VerfasserIn] |
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| | Vedula, Swaroop S. [VerfasserIn] |
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| | Cleary, Kevin [VerfasserIn] |
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| | Fichtinger, Gabor [VerfasserIn] |
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| | Forestier, Germain [VerfasserIn] |
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| | Gibaud, Bernard [VerfasserIn] |
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| | Grantcharov, Teodor [VerfasserIn] |
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| | Hashizume, Makoto [VerfasserIn] |
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| | Heckmann, Doreen [VerfasserIn] |
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| | Kenngott, Hannes Götz [VerfasserIn] |
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| | Kikinis, Ron [VerfasserIn] |
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| | Mündermann, Lars [VerfasserIn] |
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| | Navab, Nassir [VerfasserIn] |
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| | Onogur, Sinan [VerfasserIn] |
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| | Roß, Tobias [VerfasserIn] |
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| | Sznitman, Raphael [VerfasserIn] |
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| | Taylor, Russell H. [VerfasserIn] |
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| | Tizabi, Minu [VerfasserIn] |
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| | Wagner, Martin [VerfasserIn] |
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| | Hager, Gregory D. [VerfasserIn] |
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| | Neumuth, Thomas [VerfasserIn] |
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| | Padoy, Nicolas [VerfasserIn] |
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| | Collins, Justin [VerfasserIn] |
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| | Gockel, Ines [VerfasserIn] |
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| | Goedeke, Jan [VerfasserIn] |
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| | Hashimoto, Daniel A. [VerfasserIn] |
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| | Joyeux, Luc [VerfasserIn] |
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| | Lam, Kyle [VerfasserIn] |
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| | Leff, Daniel R. [VerfasserIn] |
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| | Madani, Amin [VerfasserIn] |
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| | Marcus, Hani J. [VerfasserIn] |
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| | Meireles, Ozanan [VerfasserIn] |
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| | Seitel, Alexander [VerfasserIn] |
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| | Teber, Dogu [VerfasserIn] |
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| | Ückert, Frank [VerfasserIn] |
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| | Müller, Beat P. [VerfasserIn] |
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| | Jannin, Pierre [VerfasserIn] |
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| | Speidel, Stefanie [VerfasserIn] |
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| Titel: | Surgical data science |
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| Titelzusatz: | from concepts toward clinical translation |
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| Verf.angabe: | Lena Maier-Hein, Matthias Eisenmann, Duygu Sarikaya, Keno März, Toby Collins, Anand Malpani, Johannes Fallert, Hubertus Feussner, Stamatia Giannarou, Pietro Mascagni, Hirenkumar Nakawala, Adrian Park, Carla Pugh, Danail Stoyanov, Swaroop S. Vedula, Kevin Cleary, Gabor Fichtinger, Germain Forestier, Bernard Gibaud, Teodor Grantcharov, Makoto Hashizume, Doreen Heckmann-Nötzel, Hannes G. Kenngott, Ron Kikinis, Lars Mündermann, Nassir Navab, Sinan Onogur, Tobias Roß, Raphael Sznitman, Russell H. Taylor, Minu D. Tizabi, Martin Wagner, Gregory D. Hager, Thomas Neumuth, Nicolas Padoy, Justin Collins, Ines Gockel, Jan Goedeke, Daniel A. Hashimoto, Luc Joyeux, Kyle Lam, Daniel R. Leff, Amin Madani, Hani J. Marcus, Ozanan Meireles, Alexander Seitel, Dogu Teber, Frank Ückert, Beat P. Müller-Stich, Pierre Jannin, Stefanie Speidel |
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| Jahr: | 2022 |
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| Umfang: | 46 S. |
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| Fussnoten: | Available online 18 November 2021 ; Gesehen am 31.08.2022 |
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| Titel Quelle: | Enthalten in: Medical image analysis |
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| Ort Quelle: | Amsterdam [u.a.] : Elsevier Science, 1996 |
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| Jahr Quelle: | 2022 |
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| Band/Heft Quelle: | 76(2022), Artikel-ID 102306, Seite 1-46 |
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| ISSN Quelle: | 1361-8423 |
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| 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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| DOI: | doi:10.1016/j.media.2021.102306 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
Volltext: https://doi.org/10.1016/j.media.2021.102306 |
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| | Volltext: https://www.sciencedirect.com/science/article/pii/S1361841521003510 |
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| | DOI: https://doi.org/10.1016/j.media.2021.102306 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Artificial intelligence |
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| | Clinical translation |
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| | Computer aided surgery |
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| | Deep learning |
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| | Surgical data science |
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| K10plus-PPN: | 1815460245 |
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| Verknüpfungen: | → Zeitschrift |
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