SAGES consensus recommendations on surgical video data use, structure, and exploration (for research in artificial intelligence, clinical quality improvement, and surgical education).
AI
Delphi consensus
Education
Surgical AI
Surgical data science
Journal
Surgical endoscopy
ISSN: 1432-2218
Titre abrégé: Surg Endosc
Pays: Germany
ID NLM: 8806653
Informations de publication
Date de publication:
Nov 2023
Nov 2023
Historique:
received:
04
05
2023
accepted:
05
07
2023
medline:
2
11
2023
pubmed:
30
7
2023
entrez:
29
7
2023
Statut:
ppublish
Résumé
Surgery generates a vast amount of data from each procedure. Particularly video data provides significant value for surgical research, clinical outcome assessment, quality control, and education. The data lifecycle is influenced by various factors, including data structure, acquisition, storage, and sharing; data use and exploration, and finally data governance, which encompasses all ethical and legal regulations associated with the data. There is a universal need among stakeholders in surgical data science to establish standardized frameworks that address all aspects of this lifecycle to ensure data quality and purpose. Working groups were formed, among 48 representatives from academia and industry, including clinicians, computer scientists and industry representatives. These working groups focused on: Data Use, Data Structure, Data Exploration, and Data Governance. After working group and panel discussions, a modified Delphi process was conducted. The resulting Delphi consensus provides conceptualized and structured recommendations for each domain related to surgical video data. We identified the key stakeholders within the data lifecycle and formulated comprehensive, easily understandable, and widely applicable guidelines for data utilization. Standardization of data structure should encompass format and quality, data sources, documentation, metadata, and account for biases within the data. To foster scientific data exploration, datasets should reflect diversity and remain adaptable to future applications. Data governance must be transparent to all stakeholders, addressing legal and ethical considerations surrounding the data. This consensus presents essential recommendations around the generation of standardized and diverse surgical video databanks, accounting for multiple stakeholders involved in data generation and use throughout its lifecycle. Following the SAGES annotation framework, we lay the foundation for standardization of data use, structure, and exploration. A detailed exploration of requirements for adequate data governance will follow.
Sections du résumé
BACKGROUND
BACKGROUND
Surgery generates a vast amount of data from each procedure. Particularly video data provides significant value for surgical research, clinical outcome assessment, quality control, and education. The data lifecycle is influenced by various factors, including data structure, acquisition, storage, and sharing; data use and exploration, and finally data governance, which encompasses all ethical and legal regulations associated with the data. There is a universal need among stakeholders in surgical data science to establish standardized frameworks that address all aspects of this lifecycle to ensure data quality and purpose.
METHODS
METHODS
Working groups were formed, among 48 representatives from academia and industry, including clinicians, computer scientists and industry representatives. These working groups focused on: Data Use, Data Structure, Data Exploration, and Data Governance. After working group and panel discussions, a modified Delphi process was conducted.
RESULTS
RESULTS
The resulting Delphi consensus provides conceptualized and structured recommendations for each domain related to surgical video data. We identified the key stakeholders within the data lifecycle and formulated comprehensive, easily understandable, and widely applicable guidelines for data utilization. Standardization of data structure should encompass format and quality, data sources, documentation, metadata, and account for biases within the data. To foster scientific data exploration, datasets should reflect diversity and remain adaptable to future applications. Data governance must be transparent to all stakeholders, addressing legal and ethical considerations surrounding the data.
CONCLUSION
CONCLUSIONS
This consensus presents essential recommendations around the generation of standardized and diverse surgical video databanks, accounting for multiple stakeholders involved in data generation and use throughout its lifecycle. Following the SAGES annotation framework, we lay the foundation for standardization of data use, structure, and exploration. A detailed exploration of requirements for adequate data governance will follow.
Identifiants
pubmed: 37516693
doi: 10.1007/s00464-023-10288-3
pii: 10.1007/s00464-023-10288-3
pmc: PMC10616217
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8690-8707Informations de copyright
© 2023. The Author(s).
Références
Surg Endosc. 2021 Sep;35(9):4918-4929
pubmed: 34231065
Surg Endosc. 2023 Apr;37(4):3010-3017
pubmed: 36536082
Cureus. 2021 Jun 4;13(6):e15447
pubmed: 34258114
BMC Musculoskelet Disord. 2021 May 18;22(1):451
pubmed: 34006234
IEEE Trans Med Robot Bionics. 2021 Feb;3(1):2-10
pubmed: 33644703
Ann Surg. 2022 Apr 1;275(4):e609-e611
pubmed: 35129482
Sci Rep. 2020 Dec 17;10(1):22208
pubmed: 33335191
Med Image Comput Comput Assist Interv. 2014;17(Pt 2):349-56
pubmed: 25485398
Biomed Opt Express. 2022 Apr 29;13(5):3145-3160
pubmed: 35774324
Nature. 2023 Apr;616(7956):259-265
pubmed: 37045921
Med Image Anal. 2022 Feb;76:102306
pubmed: 34879287
Surg Endosc. 2021 Jul;35(7):4008-4015
pubmed: 32720177
Surg Endosc. 2023 Jun;37(6):4321-4327
pubmed: 36729231
Int J Comput Assist Radiol Surg. 2018 Sep;13(9):1397-1408
pubmed: 30006820