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Human versus robotic organ retraction during laparoscopic Nissen fundoplication

  • Benjamin Poulose
  • Michael Kutka
  • Mario Mendoza-Sagaon
  • Aaron Barnes
  • Calvin Yang
  • Russell Taylor
  • Mark Talamini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1496)

Abstract

Advances in technique and instrumentation have enabled surgeons to perform a growing array of procedures through laparoscopy. However, these efforts have often been compromised by exerting excessive forces during retraction of the structures necessary for anatomical view. Here, we present a comparative study of human and robotic performance in force controlled organ retraction during laparoscopic Nissen fundoplication (LNF). Six female pigs (20–25 kg) were anesthetized, intubated, placed on mechanical ventilation, and pneumoperitoneum (13mm Hg CO2) was established. A force sensing retractor (FSR) was constructed to record the forces applied in retracting the stomach during dissection of the esophageal hiatus (EH). The FSR was calibrated using known forces and then operated by either human alone or robot under human guidance using the FSR data. The EH was visualized, dissected, and LNF completed. Less force was utilized with robotic (74.3±10.5 grams) versus human (108.9±34.3 grams) retraction (p=0.007) to obtain proper anatomical view of the EH. No significant differences were observed for retraction setup time (robot 14.3±0.8 minutes and human 13.7±9.9 minutes) or hiatal dissection time (robot 14.0±3.0 minutes and human 14.0±6.1 minutes). These preliminary results present our continuing effort to develop and evaluate an automated surgical assistant for laparoscopy. As increasingly advanced, personnel-intensive laparoscopic procedures are performed, robotic retraction may present a superior alternative to human retraction by minimizing the forces exerted on organs yet maintaining excellent anatomical view.

Keywords

Force Feedback Surgical Robot Esophageal Hiatus Surgical Assistant Retraction Force 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Benjamin Poulose
    • 1
  • Michael Kutka
    • 1
  • Mario Mendoza-Sagaon
    • 1
  • Aaron Barnes
    • 2
  • Calvin Yang
    • 3
  • Russell Taylor
    • 3
  • Mark Talamini
    • 1
  1. 1.Department of SurgeryJohns Hopkins University School of MedicineBaltimore
  2. 2.Department of Mechanical EngineeringJohns Hopkins UniversityBaltimore
  3. 3.Johns Hopkins University Department of Computer Science 224 New Engineering BuildingJohns Hopkins UniversityBaltimore

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