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

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.

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 ClinicalTrials.gov

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Resources

Canadian Cancer Society

These resources are provided in partnership with the Canadian Cancer Society