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
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

Pagination

82-90

Auteurs

Nicolas Padoy (N)

a ICube, IHU Strasbourg, CNRS , University of Strasbourg , Strasbourg , France.

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Classifications MeSH