An observational study in Artificial Intelligence, Endobronchial Ultrasound and Lung Cancer, sponsored by Norwegian University of Science and Technology. Recruiting at 2 sites in Norway. Open to participants aged 18 Years and older. Per ClinicalTrials.gov, last updated 2026-09-30.
Sponsored by Norwegian University of Science and Technology · Observational
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.
Patents with undiagnosed enlarged mediastinal and hilar lymph nodes who have been recommended for Endobronchial ultrasound transbronchial needle aspiration (EBUS-TBNA).
Exclusion Criteria:
Machine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level
Capability
To explore if Deep neural network (DNN) has capability to segment lymph nodes and blood vessels from EBUS images
Time frame: 8 months
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
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Norwegian University of Science and Technology