Surgical Process Modeling of Workflow and Performance in Image-Guided Bronchoscopy.

Purpose: Emerging technologies to improve transbronchial sampling of lung lesions include mobile C-arm cone-beam CT (CBCT) and robotic assistance. Surgical Process Modeling (SPM) was used to quantify performance in such procedures performed using a conventional bronchoscope with guidance via 2D fluoroscopy and radial probe endobronchial ultrasound (RP-EBUS) ("Conventional Bronchoscopy") compared to robot-assisted bronchoscopy with CBCT guidance ("CBCT-Guided RAB").

Method: Statistical SPMs were implemented for Conventional Bronchoscopy and CBCT-Guided RAB for simulation and analysis of procedural outcomes, including cycle time, radiation dose, and geometric accuracy. The SPMs were parameterized and validated with respect to clinical observation, published literature, and expert input. 9000 simulation runs were computed for each method, analyzing differences in performance and evaluating the influence of body mass index (BMI), lesion location (upper, middle, or lower lobe), and lesion size.

Results: The SPMs exhibited reasonable agreement with retrospective clinical evaluation of cycle time and dose, and variations in geometric accuracy were consistent with clinical literature. CBCT-Guided RAB resulted in a 14% increase in median cycle time (45.3 min) compared to Conventional Bronchoscopy (39.6 min) and increased median dose to the patient by 3.2 × (41.6 Gy cm² compared to 12.9 Gy cm²). Geometric targeting improved with CBCT-Guided RAB, reducing the rate of geometric miss from 22% under Conventional Bronchoscopy to 2%. 3D visualization of individual runs gave clear depiction of median and outlier performance and a basis for communicating and standardizing complex workflows.

Conclusions: SPMs yielded quantitative performance comparison in lung lesion biopsy by conventional and robot-assisted bronchoscopy. The approach quantified increases in cycle time and dose for CBCT-Guided RAB, accompanied by substantial gains in geometric accuracy. Such modeling provided valuable insight on the benefits of emerging technologies at early stages of implementation and a means to optimize and standardize clinical workflow.