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Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

ConditionsArtificial IntelligenceEndobronchial UltrasoundLung Cancer
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorNorwegian University of Science and Technology

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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

Disqualifiers

Pregnancy

Any patient that the Investigator feels is not appropriate for this study for any reason.

Trial population

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

Trial design

Design model

Case-only

Time perspective

Prospective

Treatments tested in this trial

  • machine learning algorithm

    Device

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

Treatment groups

No treatment groups listed

Trial outcomes

Primary outcomes

1

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

1

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
2

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
3

Specificity

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

Time frame
2 months
4

Dice similarity coefficient

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

Time frame
2 months

Other outcomes

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Norwegian University of Science and Technology

Lead sponsor

Helse Nord-Trøndelag HF

Collaborator

SINTEF Health Research

Collaborator

Source ClinicalTrials.gov
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