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

NCT03849040 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 52

Last updated 2020-03-11

No results posted yet for this study

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

  • Lung Diseases
  • Lung Neoplasm

Interventions

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.

Sponsors & Collaborators

  • St. Joseph's Healthcare Hamilton

    lead OTHER

Principal Investigators

  • Wael C Hanna · St. Josephs Healthcare Hamilton

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-04-08
Primary Completion
2019-09-23
Completion
2019-11-20

Countries

  • Canada

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03849040 on ClinicalTrials.gov