SAGES consensus recommendations on an annotation framework for surgical video.
Surg Endosc
; 35(9): 4918-4929, 2021 09.
Article
en En
| MEDLINE
| ID: mdl-34231065
ABSTRACT
BACKGROUND:
The growing interest in analysis of surgical video through machine learning has led to increased research efforts; however, common methods of annotating video data are lacking. There is a need to establish recommendations on the annotation of surgical video data to enable assessment of algorithms and multi-institutional collaboration.METHODS:
Four working groups were formed from a pool of participants that included clinicians, engineers, and data scientists. The working groups were focused on four themes (1) temporal models, (2) actions and tasks, (3) tissue characteristics and general anatomy, and (4) software and data structure. A modified Delphi process was utilized to create a consensus survey based on suggested recommendations from each of the working groups.RESULTS:
After three Delphi rounds, consensus was reached on recommendations for annotation within each of these domains. A hierarchy for annotation of temporal events in surgery was established.CONCLUSIONS:
While additional work remains to achieve accepted standards for video annotation in surgery, the consensus recommendations on a general framework for annotation presented here lay the foundation for standardization. This type of framework is critical to enabling diverse datasets, performance benchmarks, and collaboration.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Aprendizaje Automático
Tipo de estudio:
Guideline
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Surg Endosc
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
GASTROENTEROLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos