Training in minimally invasive thoracic surgery on 3D-model: back to the future of education
Giacomo Rabazzi, Andrea Castaldi, Vittorio Aprile, Maria Giovanna Mastromarino, Stylianos Korasidis, Sara Condino, Marina Carbone, Francesco Simi, Marcello Carlo Ambrogi, Emanuele Cigna, Marco Lucchi
- 发表年份
- 2025
- 引用次数
- 2
摘要
Minimally invasive surgery (MIS) is the standard approach for early-stage lung cancer, offering benefits such as reduced recovery time, shorter hospital stays, and minimized postoperative pain. However, these techniques require advanced motor skills and a deep understanding of thoracic anatomy. Recent technological advancements, specialized surgical instruments, and energy devices, have further improved MIS capabilities. Despite these advancements, the steep learning curve of video-assisted thoracic surgery (VATS) and robotic-assisted thoracic surgery (RATS) highlights the need for structured simulation-based education to ensure patient safety and optimize skill acquisition. Simulation training provides a risk-free environment for developing technical proficiency before operating on real patients. Virtual simulators, such as LapSim and V-Trainer, are widely used to familiarize trainees with endoscopic instruments and procedural techniques. However, real analog models remain essential for refining motor skills, depth perception, and tactile feedback, which are crucial for complex thoracic procedures. Traditional training methods using wet labs or cadaveric models pose limitations in cost, availability, and ethical concerns. The integration of high-realistic physical anatomical models, including 3D printed anatomical components, represents a promising alternative, offering high-fidelity surgical simulations that mimic real-life operative conditions. At the University of Pisa's EndoCAS Interdipartimental Center for Computer Assisted Surgery, a structured training program incorporating lung phantoms in a 3D-printed thoracic cage, virtual simulation, and stepwise lobectomy simulations have been developed to enhance thoracic surgery education. This study presents our experience with a hybrid simulation approach, based on the combination of virtual and physical simulation, in lobectomy training, emphasizing its role in bridging theoretical learning with hands-on surgical practice, ultimately improving technical skills and clinical confidence among thoracic surgery residents.
关键词
相关论文
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Martı́n Abadi, Ashish Agarwal, Paul Barham 等 20 位作者
2016
Robots and Jobs: Evidence from US Labor Markets
Daron Acemoğlu, Pascual Restrepo
2019
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm
Leigh R. Hochberg, Daniel Bacher, Beata Jarosiewicz 等 11 位作者
2012
Campbell-Walsh urology
Alan J. Wein editor-in-chief
2012