Surgical data science - from concepts toward clinical translation.
Artificial intelligence
Clinical translation
Computer aided surgery
Deep learning
Surgical data science
Journal
Medical image analysis
ISSN: 1361-8423
Titre abrégé: Med Image Anal
Pays: Netherlands
ID NLM: 9713490
Informations de publication
Date de publication:
02 2022
02 2022
Historique:
received:
10
11
2020
revised:
03
11
2021
accepted:
08
11
2021
pubmed:
9
12
2021
medline:
8
4
2022
entrez:
8
12
2021
Statut:
ppublish
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.
Identifiants
pubmed: 34879287
pii: S1361-8415(21)00351-0
doi: 10.1016/j.media.2021.102306
pmc: PMC9135051
mid: NIHMS1802848
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
102306Subventions
Organisme : NIDDK NIH HHS
ID : R01 DK123445
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB015898
Pays : United States
Organisme : NIDCR NIH HHS
ID : R01 DE025265
Pays : United States
Organisme : Wellcome Trust
ID : 203145Z/16/Z
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : NCI NIH HHS
ID : R01 CA235589
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB028741
Pays : United States
Organisme : NIBIB NIH HHS
ID : P41 EB015902
Pays : United States
Informations de copyright
Copyright © 2021. Published by Elsevier B.V.
Déclaration de conflit d'intérêts
Declaration of Competing Interest Anand Malpani is a future employee at Mimic Technologies Inc. (Seattle, WA, US). Johannes Fallert and Lars Mündermann are employed at KARL STORZ SE & Co. KG (Tuttlingen, Germany). Hirenkumar Nakawala is employed at CMR Surgical Ltd (Cambridge, UK). Nicolas Padoy is a scientific advisor of Caresyntax (Berlin, Germany). Daniel A. Hashimoto is a consultant for Johnson & Johnson (New Brunswick, NJ, USA), Verily Life Sciences (San Francisco, CA, USA), and Activ Surgical (Boston, MA, USA). He has received research support from Olympus Corporation and the Intuitive Foundation. Carla Pugh is the founder of 10 Newtons Inc. (Madison, WI, US). Danail Stoyanov is employed at Digital Surgery Ltd (London, UK) and Odin Vision Ltd (London, UK). Teodor Grantcharov is the founder of Surgical Safety Technologies Inc. (Toronto, Ontario, Canada). Tobias Roß is employed at Quality Match GmbH (Heidelberg, Germany). All other authors do not declare any conflicts of interest.
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