Digital twin-based bronchoscopy simulator improves training performance and skill retention of novices: a randomised...

Area of Science:

• Medical Simulation
• Respiratory Medicine
• Surgical Education

Background:

• Conventional bronchoscopy training lacks lasting skill retention and adaptability.
• Digital-twin simulators offer a novel approach to address these limitations.
• This study investigates the impact of diverse anatomical models in simulation-based training.

Purpose of the Study:

• To evaluate the effectiveness of training with varied, patient-derived bronchial tree models in novice bronchoscopists.
• To compare performance and skill retention between training with diverse versus uniform anatomical models.

Main Methods:

• Sixty novice participants were randomized into three groups: control, anatomic-uniformity (single model), and anatomic-variety (multiple models).
• Participants underwent initial testing and re-testing after three months on standard and novel CT-derived bronchial models.
• Performance metrics included diagnostic completeness (DC), structured progress (SP), and procedure time.

Main Results:

• No significant differences were observed between the anatomic-variety and anatomic-uniformity groups in initial testing (Test 1).
• In Test 2 (novel model), the anatomic-variety group demonstrated significantly higher DC and SP.
• Skill retention at three months showed the anatomic-variety group maintained superior performance in both tests.

Conclusions:

• Training with diverse anatomical models in bronchoscopy simulation enhances performance and adaptability.
• This method leads to better skill acquisition and retention compared to training with a single standardized model.
• Simulation using varied CT-derived bronchial models represents a promising advancement in surgical education.