Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

50 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-05-01

Study Completion Date

2027-12-01

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.

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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.

Conditions

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Artificial Intelligence Endobronchial Ultrasound Lung Cancer

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Interventions

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machine learning algorithm

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

Intervention Type DEVICE

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