Trials / Completed
CompletedNCT03849040
The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging
Development and Validation of a Computer-aided Algorithm Using Artificial Intelligence and Deep Neural Networks for the Segmentation of Ultrasonographic Features of Lymph Nodes During Endobronchial Ultrasound
- Status
- Completed
- Phase
- —
- Study type
- Observational
- Enrollment
- 52 (actual)
- Sponsor
- St. Joseph's Healthcare Hamilton · Academic / Other
- Sex
- All
- Age
- 18 Years
- Healthy volunteers
- Not accepted
Summary
This study aims to determine if a deep neural artificial intelligence (AI) network (NeuralSeg) can learn how to assign the Canada Lymph Node Score to lymph nodes examined by endobronchial ultrasound transbronchial needle aspiration(EBUS-TBNA), using the technique of segmentation. Images will be created from 300 lymph nodes videos from a prospective library and will be used as a derivation set to develop the algorithm. An additional100 lymph node images will be prospectively collected to validate if NeuralSeg can correctly apply the score.
Conditions
Interventions
| Type | Name | Description |
|---|---|---|
| PROCEDURE | Endobronchial Ultrasound | All patients will undergo EBUS-TBNA as per routine care, except for the one difference where the procedures will be video-recorded so that they can be used for computer analysis at a later time. Static images will be obtained from EBUS videos in order to perform segmentation. Segmentation will be conducted by both an experienced endoscopist and NeuralSeg. |
Timeline
- Start date
- 2019-04-08
- Primary completion
- 2019-09-23
- Completion
- 2019-11-20
- First posted
- 2019-02-21
- Last updated
- 2020-03-11
Locations
1 site across 1 country: Canada
Source: ClinicalTrials.gov record NCT03849040. Inclusion in this directory is not an endorsement.