| Title: |
Simulation Training in Minimally Invasive Lung Resections: Developing a Reproducible, High-Fidelity Porcine Model |
| Authors: |
Whooley, J; O’Conghaile, C; Horne, S; O’Keefe, DA; Condron, C; Fitzmaurice, GJ; McGuire, BB |
| Publisher Information: |
Springer Science and Business Media LLC |
| Publication Year: |
2025 |
| Description: |
Robotic-assisted lung resection has gained prominence within the era of minimally invasive surgery as it holds the potential to offer improved peri-operative pain and duration of stay without compromising oncological outcomes. The surgeon learning curve remains a challenge and simulation forms a crucial part of surgical training. Virtual reality simulation is currently the standard modality used by trainees when simulating console operating, which lacks the ability to replicate real tissue handling. We describe a high-fidelity, reproducible, low-cost porcine lung model from adult Landrace pigs, with porcine peri-cardiac fat used to simulate pulmonary tumours. This model was evaluated in a training session for higher-level cardiothoracic trainees performing robotic-assisted lung resections under the guidance of expert robotic surgeons with feedback recorded via an evaluation tool. Trainees rated the model high on its fidelity to human lung simulation, tissue handling and overall usefulness (median score 4/5). Trainees reported that this model was very useful for simulating realistic lung parenchymal manipulation, wedge resections and hilar dissection, while suggestions for improvement included adding simulated blood flow. This is a low-cost, high-fidelity simulation model for robotic-assisted lung resection with high acceptability to surgical trainees, which could be readily adopted by other training centres. |
| Document Type: |
other/unknown material |
| Language: |
unknown |
| DOI: |
10.21203/rs.3.rs-7010617/v1 |
| Availability: |
https://doi.org/10.21203/rs.3.rs-7010617/v1; https://www.researchsquare.com/article/rs-7010617/v1; https://www.researchsquare.com/article/rs-7010617/v1.html |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.C5B042E7 |
| Database: |
BASE |