Machine and deep learning for workflow recognition during surgery.

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 p...

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Publicado en:Minimally Invasive Therapy & Allied Technologies Vol. 28; no. 2; pp. 82 - 91
Autor principal: Padoy, Nicolas
Formato: pictorial review tables/charts Journal Article
Publicado: Taylor & Francis Ltd Apr2019
Acceso en línea:Ver este registro en EBSCOhost
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      pub: Taylor & Francis Ltd
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        atl: Machine and deep learning for workflow recognition during surgery.
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        au: Padoy, Nicolas
        affil: ICube, IHU Strasbourg, CNRS, University of Strasbourg, Strasbourg, France
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        subj:
          Machine Learning
          Deep Learning
          Surgery, Operative
          Workflow
          Operating Rooms Administration
          Videorecording
          Equipment and Supplies
          Systems Analysis
      ab: 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.
      pubtype: Academic Journal
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        review
        tables/charts
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    language: English
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