NCT05739331

Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using a Deep Neural Network

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 50 (estimated)
• Sponsor: Norwegian University of Science and Technology · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

To evaluate the usefulness of Deep neural network (DNN) in the evaluation of mediastinal and hilar lymph nodes with Endobronchial ultrasound (EBUS). The study will explore the feasibility of DNN to identify lymph nodes and blood vessel examined with EBUS.

Detailed description

Multi-center prospective feasibility study. The DNN model will be trained on ultrasound images with annotation to identifies lymph nodes and blood vessels examined with EBUS. The ability of the DNN to segment lymph nodes and vessels based on postoperative processing and static EBUS images will be evaluated in the first part of the study. In the second part of the study Real-time use of DNN in EBUS procedure will be evaluated.

Conditions

• Artificial Intelligence
• Endobronchial Ultrasound
• Lung Cancer

Interventions

Type	Name	Description
DEVICE	machine learning algorithm	Machine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level

Timeline

• Start date: 2023-05-01
• Primary completion: 2027-05-01
• Completion: 2027-12-01
• First posted: 2023-02-22
• Last updated: 2025-08-22

Locations

2 sites across 1 country: Norway