Machine and deep learning for workflow recognition during surgery.
Activity recognition
RGB-D video
clinician pose estimation
endoscopic video
operating room
surgical control tower
surgical workflow
tool detection
Journal
Minimally invasive therapy & allied technologies : MITAT : official journal of the Society for Minimally Invasive Therapy
ISSN: 1365-2931
Titre abrégé: Minim Invasive Ther Allied Technol
Pays: England
ID NLM: 9612996
Informations de publication
Date de publication:
Apr 2019
Apr 2019
Historique:
pubmed:
9
3
2019
medline:
25
6
2019
entrez:
9
3
2019
Statut:
ppublish
Résumé
Recent years have seen tremendous progress in artificial intelligence (AI), such as with the automatic and real-time recognition of objects and activities in videos in the field of computer vision. Due to its increasing digitalization, the operating room (OR) promises to directly benefit from this progress in the form of new assistance tools that can enhance the abilities and performance of surgical teams. Key for such tools is the recognition of the surgical workflow, because efficient assistance by an AI system requires this system to be aware of the surgical context, namely of all activities taking place inside the operating room. We present here how several recent techniques relying on machine and deep learning can be used to analyze the activities taking place during surgery, using videos captured from either endoscopic or ceiling-mounted cameras. We also present two potential clinical applications that we are developing at the University of Strasbourg with our clinical partners.
Identifiants
pubmed: 30849261
doi: 10.1080/13645706.2019.1584116
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM