Artificial IntelligenceNCT05739331
Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence
An observational study of Artificial Intelligence and Endobronchial Ultrasound, run by Norwegian University of Science and Technology at 2 sites in Norway.
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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artificial Intelligence, Endobronchial Ultrasound, Lung Cancer
- What is being tested?
- machine learning algorithm
- Type of treatment
- Medical device
- How many people
- 50 participants
- Where
- 2 sitesNorway
- Study dates
- 1 May 2023to 1 Dec 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patents with undiagnosed enlarged mediastinal and hilar lymph nodes who have been recommended for Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA).
Who may be able to join
- 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
Who cannot join
- Pregnancy
- Any patient that the Investigator feels is not appropriate for this study for any reason.
Trial locations and contacts
2 sites in 1 country. Contact the study team or a site near you to ask about taking part.
Norway2 sites, 2 recruiting
- Recruiting
Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust
Levanger, 7600
Øyvind Ervik, MD, +4791634595, oyvind.ervik@ntnu.no
- Recruiting
Department of Thoracic Medicine, St Olavs Hospital
Trondheim, 7030
Håkon O Leira, MD, PhD, +4799014967, håkon.o.leira@ntnu.no
About this study
Official title: Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using a Deep Neural Network
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.
Read the 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.
- Being tested
- machine learning algorithm
- Medical subject headings
- Lung Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
machine learning algorithmDevice
Machine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level
What is being measured
The primary outcome is the main question the study is designed to answer.
Capability
To explore if Deep neural network (DNN) has capability to segment lymph nodes and blood vessels from EBUS images
Measured: 8 months
Secondary outcomes (6)
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).
Measured: 2 months
Sensitivity
True positive rate. Correctly detected lymph nodes/blood vessel over total lymph nodes/blood vessel. Measured per pixel in the EBUS images
Measured: 2 months
Specificity
Specificity = (True Negative)/(True Negative + False Positive). Measured per pixel in the EBUS images.
Measured: 2 months
Dice similarity coefficient
Measures the similarity between two sets of data: Annotated by pulmonologist vs DNN.
Measured: 2 months
Run-time
Is the run-time sufficiently low for real-time analysis during EBUS?
Measured: 2 months
Adverse events
Procedure related adverse events or unexpected incidents registered
Measured: 48 hours
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Feb 2023
- Study start1 May 2023
- Primary completion1 May 2027 (estimated)
- Study completion1 Dec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Norwegian University of Science and Technology
- Sponsor type
- University, hospital or other
- Collaborators
- Helse Nord-Trøndelag HF, SINTEF Health Research
- Responsible party
- Sponsor
- Investigators
- Øivind Rognmo, Dr.philos, Study director, Norwegian University of Science and Technology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05739331
- Sponsor's protocol ID
- 240245
- Status last verified
- Sep 2026
- First submitted
- 13 Feb 2023
- Last updated
- 30 Sep 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 30 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using a Deep Neural Network
- ClinicalTrials.gov ID
- NCT05739331
- Acronym / sponsor ID
- 240245
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Norwegian University of Science and TechnologyUniversity, hospital or other
- Collaborators
- Helse Nord-Trøndelag HF, SINTEF Health Research
- Study start
- 1 May 2023 (actual)
- Primary completion
- 1 May 2027 (estimated)
- Study completion
- 1 Dec 2027 (estimated)
- First posted
- 22 Feb 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patents with undiagnosed enlarged mediastinal and hilar lymph nodes who have been recommended for Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA).
Key inclusion criteria (2 listed)
- 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
Key exclusion criteria (2 listed)
- Pregnancy
- Any patient that the Investigator feels is not appropriate for this study for any reason.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| machine learning algorithmMachine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level | Device | – |
Primary outcome
Capability
Time frame: 8 months
To explore if Deep neural network (DNN) has capability to segment lymph nodes and blood vessels from EBUS images
6 secondary outcomes
Precision
Time frame: 2 months
Sensitivity
Time frame: 2 months
Specificity
Time frame: 2 months
Dice similarity coefficient
Time frame: 2 months
Run-time
Time frame: 2 months
Adverse events
Time frame: 48 hours
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 30 Sep 2026.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Øivind Rognmo, Dr.philos study director
Norwegian University of Science and Technology
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Artificial Intelligence and Endobronchial Ultrasound, run by Norwegian University of Science and Technology at 2 sites in Norway.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 30 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Norway. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Øyvind Ervik, MD (+4791634595, oyvind.ervik@ntnu.no), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Capability (time frame: 8 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 30 Sep 2026.