Automated Skill Evaluation of Bronchoscopy Operators Using Geometric Feature Analysis

Bronchoscopy is a critical procedure for diagnosing and treating lung conditions, but current training assessments are subjective and time-consuming. More objective skill assessment methods are needed to ensure clinician competence. We propose an automatic skill assessment method to classify bronchoscope operators as experts or novices. Pose data from an electromagnetic tracker was collected during bronchoscopy on a lung model. We define geometric features that capture key aspects of operator performance and used them to train a classifier distinguishing experts from novices. Our results show that expert operators adhere more closely to the geometric features, with smaller deviations and greater consistency, while novice operators exhibit higher variance. Statistical analysis confirmed significant differences between expert and novice performance. The classifier achieved high accuracy, with precision and recall ranging from 0.89 to 1.0 across tasks. This automated skill assessment method effectively classifies bronchoscopy operators and offers a potential tool for improving training and developing robotic bronchoscopy systems.