Robotic scrub nurse to anticipate surgical instruments based on real-time laparoscopic video analysis.

Autor: Wagner L; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany. lars.wagner@tum.de., Jourdan S; Technical University of Darmstadt, Software & Digital Business Group, Darmstadt, Germany., Mayer L; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany., Müller C; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany., Bernhard L; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany., Kolb S; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany., Harb F; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany., Jell A; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany.; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Munich, Germany., Berlet M; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany.; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Munich, Germany., Feussner H; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany.; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Munich, Germany., Buxmann P; Technical University of Darmstadt, Software & Digital Business Group, Darmstadt, Germany., Knoll A; Technical University of Munich, TUM School of Computation, Information and Technology, Chair of Robotics, Artificial Intelligence and Real-Time Systems, Garching, Germany., Wilhelm D; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Research Group MITI, Munich, Germany.; Technical University of Munich, TUM School of Medicine and Health, Klinikum rechts der Isar, Department of Surgery, Munich, Germany.; Technical University of Munich, Munich Institute of Robotics and Machine Intelligence, Munich, Germany.
Jazyk: angličtina
Zdroj: Communications medicine [Commun Med (Lond)] 2024 Aug 02; Vol. 4 (1), pp. 156. Date of Electronic Publication: 2024 Aug 02.
DOI: 10.1038/s43856-024-00581-0
Abstrakt: Background: Machine learning and robotics technologies are increasingly being used in the healthcare domain to improve the quality and efficiency of surgeries and to address challenges such as staff shortages. Robotic scrub nurses in particular offer great potential to address staff shortages by assuming nursing tasks such as the handover of surgical instruments.
Methods: We introduce a robotic scrub nurse system designed to enhance the quality of surgeries and efficiency of surgical workflows by predicting and delivering the required surgical instruments based on real-time laparoscopic video analysis. We propose a three-stage deep learning architecture consisting of a single frame-, temporal multi frame-, and informed model to anticipate surgical instruments. The anticipation model was trained on a total of 62 laparoscopic cholecystectomies.
Results: Here, we show that our prediction system can accurately anticipate 71.54% of the surgical instruments required during laparoscopic cholecystectomies in advance, facilitating a smoother surgical workflow and reducing the need for verbal communication. As the instruments in the left working trocar are changed less frequently and according to a standardized procedure, the prediction system works particularly well for this trocar.
Conclusions: The robotic scrub nurse thus acts as a mind reader and helps to mitigate staff shortages by taking over a great share of the workload during surgeries while additionally enabling an enhanced process standardization.
(© 2024. The Author(s).)
Databáze: MEDLINE
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