Conditional Autonomy in Robot-Assisted Transbronchial Interventions

Lung cancer is one of the leading causes of cancer-related deaths, and accurate staging is critical for determining the appropriate treatment. Robotic Navigation Bronchoscopy has shown advantages over traditional manual procedures, offering benefits in safety, efficiency, and accessibility. Although there is ongoing discussion regarding autonomous RNB, there is limited focus on the autonomy in advancing the bronchoscope. In this study, we introduce a novel method for conditional autonomy in advancing and aligning a robotic bronchoscope, which was validated in vitro, ex vivo, and in vivo. This conditional autonomy utilizes a monoscopic bronchoscopic view as input, with operators guiding the system by specifying the next airway to enter at branching points. The reachability of target lesions using this conditional autonomy was 73.3% in the phantom study and 77.5% in the ex vivo study. Statistical significance was found in success rates between bifurcations and trifurcations (p = 0.03) and across lobe segments (p = 0.005). The presence of breathing motion did not affect lesion reachability or the success of turns at branching points in the ex vivo studies. In the in vivo study, when comparing conditional automation to human-operated navigation, the conditional automation took less time to reach the target lesions than human operators. The median time for passing each bifurcation was 2.5 seconds for human operators and 1.3 seconds for conditional automation. By improving precision and consistency in tissue sampling, this technology could redefine the standard of care for lung cancer patients, leading to more accurate diagnoses and therapies.