Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

Norwegian University of Science and Technology50 enrolled

Overview

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.

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 Type

OBSERVATIONAL

Enrollment

Conditions

Artificial Intelligence Endobronchial Ultrasound Lung Cancer

Interventions

machine learning algorithmDEVICE

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

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

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.

Locations (2)

Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust

Levanger, Norway

RECRUITING

Department of Thoracic Medicine, St Olavs Hospital

Trondheim, Norway

RECRUITING

Outcomes

Primary Outcomes

Capability

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

Time frame: 8 months

Secondary Outcomes

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).

Time frame: 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

Time frame: 2 months

Specificity

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

Time frame: 2 months

Dice similarity coefficient

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

Time frame: 2 months

Run-time

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

Time frame: 2 months

Adverse events

Procedure related adverse events or unexpected incidents registered

Time frame: 48 hours

Central Contacts

Øyvind Ervik, MD

CONTACT

+4791634595 oyvind.ervik@ntnu.no

Hanne Sorger, MD,PhD

CONTACT

+4791816787 hanne.sorger@ntnu.no

Data from ClinicalTrials.gov

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