Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

Lung Cancer Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05739331
• Other study ID #: 240245
• Status: Recruiting
• Start date: May 1, 2023
• Est. completion date: December 1, 2025

Study information

• Verified date: November 2023
• Source: Norwegian University of Science and Technology
• Contact: Øyvind Ervik, MD
• Phone: +4791634595
• Email: oyvind.ervik[@]ntnu.no
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 50
• Est. completion date: December 1, 2025
• Est. primary completion date: May 1, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Endobronchial Ultrasound
• Lung Cancer

Intervention

Device:
• machine learning algorithm
Machine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level

Locations

Country	Name	City	State
Norway	Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust	Levanger
Norway	Department of Thoracic Medicine, St Olavs Hospital	Trondheim

Sponsors (3)

Lead Sponsor	Collaborator
Norwegian University of Science and Technology	Helse Nord-Trøndelag HF, SINTEF Health Research

Country where clinical trial is conducted

Norway, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Capability	To explore if Deep neural network (DNN) has capability to segment lymph nodes and blood vessels from EBUS images	8 months
Secondary	Precision	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).	2 months
Secondary	Sensitivity	True positive rate. Correctly detected lymph nodes/blood vessel over total lymph nodes/blood vessel. Measured per pixel in the EBUS images	2 months
Secondary	Specificity	Specificity = (True Negative)/(True Negative + False Positive). Measured per pixel in the EBUS images.	2 months
Secondary	Dice similarity coefficient	Measures the similarity between two sets of data: Annotated by pulmonologist vs DNN.	2 months
Secondary	Run-time	Is the run-time sufficiently low for real-time analysis during EBUS?	2 months
Secondary	Adverse events	Procedure related adverse events or unexpected incidents registered	48 hours