Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Artificial Intelligence, Endobronchial Ultrasound, Lung Cancer

Intervention / Treatment
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* Device: machine learning algorithm


Participation Criteria
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Eligibility Criteria
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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.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Device: machine learning algorithm|Machine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | 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 Outcome Measures

| Outcome Measure | 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 | True positive rate. Correctly detected lymph nodes/blood vessel over total lymph nodes/blood vessel. Measured per pixel in the EBUS images | 2 months | 
| Specificity | Specificity = (True Negative)/(True Negative + False Positive). Measured per pixel in the EBUS images. | 2 months | 
| Dice similarity coefficient | Measures the similarity between two sets of data: Annotated by pulmonologist vs DNN. | 2 months | 
| Run-time | Is the run-time sufficiently low for real-time analysis during EBUS? | 2 months | 
| Adverse events | Procedure related adverse events or unexpected incidents registered | 48 hours |