A Model-Based Framework for Assessing Operator Performance in Navigational Bronchoscopy

Abstract

Bronchoscopy is a critical procedure for diagnosing and treating pulmonary diseases, but its safe and effective execution demands substantial operator training. Insufficient experience is associated with higher complication rates, including bleeding, pneumothorax, and bronchospasm. Existing assessment tools provide structured evaluations, yet they remain heavily reliant on subjective expert judgment and limited sensory feedback. To address this limitation, we propose a model-based framework for objective performance evaluation in navigational bronchoscopy. Our approach leverages pose data from electromagnetic (EM) trackers, routinely used in clinical navigation, and embeds nonholonomic kinematic constraints that characterize expert-like trajectories. Using the model and a Model Predictive Path Integral (MPPI) control, we generate optimal reference trajectories and define error metrics that quantify deviations between operator-executed and model-predicted motions. We hypothesize that these deviations provide robust discriminative features for distinguishing between expert and novice performance. Experiments on a phantom lung dataset comprising 11 operators and 98 procedures demonstrate that the proposed metrics significantly separate skill levels, enabling the construction of an effective classifier for operator proficiency. This framework offers an interpretable, data-driven alternative to supervisor-dependent assessments and represents a step toward scalable, objective skill evaluation and transfer in bronchoscopy training and robotic platforms.