Augmented Endobronchial Ultrasound (EBUS-TBNA) With Artificial Intelligence
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: machine learning algorithm.
- Who it may be relevant to
- Registry conditions: Artificial Intelligence, Endobronchial Ultrasound, Lung Cancer. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Norway
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Automatic Segmentation of Mediastinal Lymph Nodes and Blood Vessels in Endobronchial Ultrasound (EBUS) Images Using a Deep Neural Network
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.
Detailed description
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.
Interventions
- Device machine learning algorithm
Machine learning algorithm run on EBUS images for real-time labelling of mediastinal lymph nodes and lymph node level
Primary outcome measures
- Capability [Time frame: 8 months]
Secondary outcome measures (6)
- Precision [Time frame: 2 months]
- Sensitivity [Time frame: 2 months]
- Specificity [Time frame: 2 months]
- Dice similarity coefficient [Time frame: 2 months]
- Run-time [Time frame: 2 months]
- Adverse events [Time frame: 48 hours]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Case-only
Study locations
Norway · 2 centers
- Department of Pulmonology, Levanger Hospital, North Trøndelag Hospital Trust — Levanger
- Department of Thoracic Medicine, St Olavs Hospital — Trondheim
Identifiers
NCT: NCT05739331 · 240245