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A Novel Competency-Based Simulation Model for Thoracoscopic Lung Resection.

Ganwei Liu1,2, Feng Yang1,2, Zuli Zhou1,2

  • 1Department of Thoracic Surgery, Peking University People's Hospital, Beijing, People's Republic of China.

The Thoracic and Cardiovascular Surgeon
|September 16, 2025
PubMed
Summary
This summary is machine-generated.

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High-fidelity 3D-printed lung models significantly improve surgical residents' anatomical knowledge and technical skills in thoracoscopic procedures compared to traditional trainers. This simulation enhances confidence and performance in thoracic surgery training.

Area of Science:

  • Medical Education
  • Surgical Simulation
  • Thoracic Surgery

Background:

  • Conventional box trainers offer limited anatomical accuracy and tactile feedback for complex thoracic procedures.
  • High-fidelity 3D-printed lung models present a more realistic simulation environment for surgical training.
  • The educational impact of advanced 3D-printed models in thoracic surgery requires further investigation.

Purpose of the Study:

  • To compare the effectiveness of high-fidelity 3D-printed lung models versus conventional box trainers in thoracic surgery simulation.
  • To evaluate the impact on residents' anatomical comprehension, technical skills, and confidence.

Main Methods:

  • Fifty-two surgical residents without prior thoracoscopic experience were randomized into two groups: high-fidelity lung model or Fundamentals of Laparoscopic Surgery (FLS) box trainer.

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  • Participants underwent standardized training, followed by anatomy tests and a simulated thoracoscopic lung wedge resection.
  • Performance was assessed using the Objective Structured Assessment of Technical Skill (OSATS) and self-reported confidence.

    • Main Results:

    • The high-fidelity lung model group demonstrated significantly greater improvements in anatomy scores (87.6% vs. 78.2%) compared to the FLS group.
    • Residents using the 3D-printed models achieved higher OSATS scores (19.18 vs. 15.41) and reported increased confidence (3.13 vs. 2.27).
    • Baseline anatomy scores were comparable between the groups, indicating similar starting knowledge.

    Conclusions:

    • High-fidelity 3D-printed lung models are superior to conventional box trainers for enhancing anatomical understanding in thoracic surgery residents.
    • These advanced models significantly improve technical proficiency and confidence in performing thoracoscopic procedures.
    • Integrating anatomically accurate simulations is crucial for optimizing cognitive and psychomotor outcomes in surgical education.