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

Lars Wagner, Sara Jourdan, Leon Mayer, Carolin Müller, Lukas Bernhard, Sven Kolb, Farid Harb, Alissa Jell, Maximilian Berlet, Hubertus Feussner, Peter Buxmann, Alois Knoll and Dirk Wilhelm

Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL)

Abstract: 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.

Date: 2024-08-02
Note: for complete metadata visit http://tubiblio.ulb.tu-darmstadt.de/149347/
References: View complete reference list from CitEc
Citations:

Published in Nature Communications Medicine (2024-08-02)

Downloads: (external link)
https://www.nature.com/articles/s43856-024-00581-0

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:dar:wpaper:149347

Access Statistics for this paper

More papers in Publications of Darmstadt Technical University, Institute for Business Studies (BWL) from Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL) Contact information at EDIRC.
Bibliographic data for series maintained by Dekanatssekretariat ().