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Sensorized Vascular High-Fidelity Physical Simulator for Robot-Assisted Surgery Training: A Multisite Pilot

Giulia Gamberini1,2,3, Alessandro Dario Mazzotta2,3,4, Angela Durante1,5

  • 1Health Science Interdisciplinary Center, Sant'Anna School of Advanced Studies, 56127 Pisa, Italy.

Journal of Clinical Medicine
|February 13, 2026
PubMed
Summary
This summary is machine-generated.

This study developed a surgical simulator to assess expertise levels in Robot-Assisted Surgery (RAS). The simulator effectively differentiated skill levels and showed potential for surgical training by measuring tissue deformation.

Keywords:
discriminant validityhigh-fidelity physical simulatorrobotic surgical trainingvascular structurevessel deformation

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Area of Science:

  • Biomedical Engineering
  • Surgical Simulation Technology
  • Robotics in Medicine

Background:

  • Robot-Assisted Surgery (RAS) lacks haptic feedback, complicating skill acquisition and potentially leading to adverse events.
  • Objective assessment of surgical skills in RAS is crucial for improving training and patient safety.
  • Current training methods may not adequately prepare surgeons for the nuances of RAS, particularly regarding tactile feedback.

Purpose of the Study:

  • To evaluate a novel surgical simulator's ability to discriminate between different levels of surgical expertise.
  • To explore potential differences in performance and skill assessment across various surgical specialties using the simulator.
  • To validate the simulator's realism and usability for surgical training applications.

Main Methods:

  • Development of a simulator replicating vascular and adipose tissue with integrated resistive stretching sensors to measure deformation.
  • 30 participants (21 general surgeons, 2 thoracic surgeons, 4 gynecologists, 3 urologists) performed standardized surgical tasks.
  • Objective measurement of tissue deformation and subjective assessment via questionnaires on face/content validity and usability.

Main Results:

  • The simulator demonstrated discriminant validity, with significant differences noted in maximum and mean deformation values (p<0.05).
  • Significant differences in performance were observed between urologists and general surgeons (p=0.0167), and urologists and gynecologists (p=0.0495).
  • High positive responses for face validity (80%) and content validity (90%) indicate good simulator realism and relevance.

Conclusions:

  • The developed surgical simulator effectively differentiates between surgical expertise levels based on objective tissue deformation metrics.
  • The simulator shows promise in identifying performance variations across different surgical specialties.
  • Further research will focus on evaluating the simulator's efficacy in actual surgical training programs.