AI-Driven Autonomous Registration in Robotic Bronchoscopy

Shanghai Jiao Tong University

Status: Not yet enrolling

Conditions: Localization Efficiency; Bronchoscopy; Lung Nodules

Treatments: Device: AI-driven autonomous registration

Study type: Interventional

Funder types: Other

Identifiers

NCT07368829

RTS-030

Details and patient eligibility

About

This study aims to evaluate the feasibility and safety of an artificial intelligence (AI)-driven autonomous registration technology in robotic navigational bronchoscopy. A total of 20 patients with pulmonary nodules requiring localization will be enrolled. The Langhe Bronchoscopy Robot System equipped with AI-based autonomous registration software will be used. Primary outcomes include the success rate of autonomous registration and the rate of manual intervention during the process. Secondary outcomes encompass registration time, complication rates, and nodule localization success.

Enrollment: 20 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Age ≥ 18 years.
• Radiologically confirmed pulmonary nodules requiring preoperative localization.
• Scheduled for robotic navigational bronchoscopy using the Bronchoscopy Robot System.
• Willing to provide written informed consent.

Exclusion criteria

• Severe cardiopulmonary dysfunction (e.g., FEV1 < 30% predicted).
• Coagulopathy or anticoagulation therapy that cannot be safely interrupted.
• Pregnancy or lactation.
• Inability to tolerate bronchoscopy under general anesthesia.

Trial design

Primary purpose: Device Feasibility

Allocation: N/A

Interventional model: Single Group Assignment

Masking: None (Open label)

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20 participants in 1 patient group

Autonomous registration group

Experimental group

Description:

All participants in this arm will undergo robotic navigational bronchoscopy and pulmonary nodule localization performed using the Langhe Bronchoscopy Robot System. The key intervention is the use of artificial intelligence (AI)-driven autonomous registration technology to automatically align the pre-operative chest CT images with the real-time bronchoscopic anatomy prior to the procedure. This process aims to reduce reliance on the conventional, operator-dependent manual registration. Physicians will supervise the entire process and perform necessary manual intervention if the AI registration is unsatisfactory or for safety reasons.

Treatment:

Device: AI-driven autonomous registration