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This study introduces conditional autonomy for robotic navigation bronchoscopy, improving lung cancer lesion reachability and reducing navigation time. This advancement enhances diagnostic accuracy and treatment for lung cancer patients.

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Area of Science:

Medical Robotics
Pulmonology
Oncology

Background:

Lung cancer is a leading cause of cancer death, necessitating accurate staging for effective treatment.
Robotic Navigation Bronchoscopy (RNB) offers safety and efficiency advantages over manual methods.
Limited research exists on bronchoscope advancement autonomy in RNB.

Purpose of the Study:

To introduce and validate a novel method for conditional autonomy in advancing and aligning a robotic bronchoscope.
To assess the reachability of target lesions using this autonomous system.
To compare the navigation speed of conditional autonomy versus human operation in vivo.

Main Methods:

Developed a conditional autonomy system for robotic bronchoscopy using monoscopic view.
Operators guided the system by selecting the next airway at branching points.
Validated the system in vitro, ex vivo (including simulated breathing motion), and in vivo studies.

Main Results:

Achieved 73.3% lesion reachability in phantom and 77.5% in ex vivo studies.
Demonstrated statistically significant differences in success rates between bifurcations/trifurcations and across lung lobe segments.
In vivo studies showed conditional autonomy reached target lesions faster than human operators (1.3s vs 2.5s per bifurcation).

Conclusions:

Conditional autonomy for robotic bronchoscope advancement is feasible and effective.
This technology improves navigation efficiency and lesion accessibility in the lungs.
Potential to enhance precision in tissue sampling, redefining lung cancer diagnosis and care.