Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

Recruiting

• Conditions: Endobronchial Ultrasound; Artificial Intelligence; Lung Cancer

• Registration Number: NCT05739331
• Lead Sponsor: Norwegian University of Science and Technology

Brief 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.

Study Timeline

• Study First Submitted: 2023-02-13
• Study First Submitted QC: 2023-02-13
• Study First Posted: 2023-02-22
• Study Start: 2023-05-01
• Status Verified: 2023-11
• Last Update Submitted: 2023-11-15
• Last Update Posted: 2023-11-18
• Primary Completion: 2025-05-01
• Study Completion: 2025-12-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 50

Inclusion Criteria

• Subjects referred to thoracic department in any of the participating hospitals with undiagnosed enlarged mediastinal and hilar lymph nodes.
• Subjects have to be ≥ 18 years of age

Exclusion Criteria

• Pregnancy
• Any patient that the Investigator feels is not appropriate for this study for any reason.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Capability	8 months	To explore if Deep neural network (DNN) has capability to segment lymph nodes and blood vessels from EBUS images

Secondary Outcome Measures

Name	Time	Method
Dice similarity coefficient	2 months	Measures the similarity between two sets of data: Annotated by pulmonologist vs DNN.
Run-time	2 months	Is the run-time sufficiently low for real-time analysis during EBUS?
Precision	2 months	The precision the DNN has for detecting lymph nodes and blood vessels. Measured both per voxel in the EBUS images and per annotated structure (a structure is counted as detected if at least 50% of its annotated pixels are identified by the DNN).
Adverse events	48 hours	Procedure related adverse events or unexpected incidents registered
Sensitivity	2 months	True positive rate. Correctly detected lymph nodes/blood vessel over total lymph nodes/blood vessel. Measured per pixel in the EBUS images
Specificity	2 months	Specificity = (True Negative)/(True Negative + False Positive). Measured per pixel in the EBUS images.

Trial Locations

Locations (2)

Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust; 🇳🇴 Levanger, Norway

Department of Thoracic Medicine, St Olavs Hospital; 🇳🇴 Trondheim, Norway