The Use of Artificial Intelligence to Predict Cancerous Lymph Nodes for Lung Cancer Staging During Ultrasound Imaging

Official Title

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


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.

Trial Description

Primary Outcome:

  • Development of computer algorithm to identify lymph node ultrasonographic features
  • Validation of computer algorithm to identify lymph node ultrasonographic features
Secondary Outcome:
  • Accuracy and reliability of the segmentation performed by NeuralSeg
  • NeuralSeg prediction of lymph node malignancy

View this trial on

Interested in this trial?

Print this page and take it to your doctor to discuss your eligibilty and treatment options. Only your doctor can refer you to a clinical trial.


Canadian Cancer Society

These resources are provided in partnership with the Canadian Cancer Society