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NCT Number: NCT05739331

Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

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.

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Key information

Conditions

Artificial Intelligence Endobronchial Ultrasound Lung Cancer Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust, Levanger, Norway

Loading trial locations.

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

machine learning algorithm

Device

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

Primary outcomes

  1. Capability

    Time frame: 8 months

    To explore if Deep neural network (DNN) has capability to segment lymph nodes and blood vessels from EBUS images

Secondary outcomes

  1. Precision

    Time frame: 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).

  2. Sensitivity

    Time frame: 2 months

    True positive rate. Correctly detected lymph nodes/blood vessel over total lymph nodes/blood vessel. Measured per pixel in the EBUS images

  3. Specificity

    Time frame: 2 months

    Specificity = (True Negative)/(True Negative + False Positive). Measured per pixel in the EBUS images.

  4. Dice similarity coefficient

    Time frame: 2 months

    Measures the similarity between two sets of data: Annotated by pulmonologist vs DNN.

  5. Run-time

    Time frame: 2 months

    Is the run-time sufficiently low for real-time analysis during EBUS?

  6. Adverse events

    Time frame: 48 hours

    Procedure related adverse events or unexpected incidents registered

Study contacts

Contact information is provided by the study sponsor or research team.

Hanne Sorger, MD,PhD

CONTACT

[email protected]

+4791816787

Øyvind Ervik, MD

CONTACT

[email protected]

+4791634595

Sponsors and collaborators

Lead sponsor

Norwegian University of Science and Technology

Other

Collaborators

  • Helse Nord-Trøndelag HF
  • SINTEF Health Research

Registry information

Official study title

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

Important dates

Study start
2023
Primary completion
2027
Study completion
2027
First posted
Feb 22, 2023
Registry last updated
Aug 22, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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