Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artif... | NCT05739331

Studies
Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

NCT05739331

Sponsor

Norwegian University of Science and Technology

Status

Recruiting

Last Update Posted

Aug 22, 2025Actual

Enrollment

50Estimated

Phase

Not provided

Conditions

Artificial Intelligence
Endobronchial Ultrasound
Lung Cancer

Interventions

machine learning algorithm

Countries

Norway

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Jun 2025

Overall Recruitment Status or Expanded Access Status

Recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

May 1, 2023Actual

Primary Completion Date

May 1, 2027Estimated

Completion Date

Dec 1, 2027Estimated

First Submitted Date

Feb 13, 2023

First Submission Date that Met QC Criteria

Feb 13, 2023

First Posted Date

Feb 22, 2023Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Aug 18, 2025

Last Update Posted Date

Aug 22, 2025Actual

Name	Class
Helse Nord-Trøndelag HF	OTHER
SINTEF Health Research	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

50Estimated

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
machine learning algorithm	Device	Machine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level

Outcomes Module

Primary Outcomes

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

Secondary Outcomes

Measure	Description	Time Frame
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
Sensitivity

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

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.

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

Patents with undiagnosed enlarged mediastinal and hilar lymph nodes who have been recommended for Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA).

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Øyvind Ervik, MD	Contact	+4791634595		oyvind.ervik@ntnu.no
Hanne Sorger, MD,PhD	Contact	+4791816787		hanne.sorger@ntnu.no

Overall Officials

Name	Affiliation	Role
Øivind Rognmo, Dr.philos	Norwegian University of Science and Technology	Study Director

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust	Recruiting	Levanger		7600	Norway

Condition Browse Module

MeSH Terms

ID	Term
D008175	Lung Neoplasms

Ancestor Terms

ID	Term
D012142	Respiratory Tract Neoplasms
D013899	Thoracic Neoplasms
D009371	Neoplasms by Site
D009369	Neoplasms

Browse Leaves

Not provided

Browse Branches

Not provided