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Prediction Model of CP-EBUS in the Diagnosis of Lymph Nodes

NCT04328792 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1300

Last updated 2020-04-02

No results posted yet for this study

Summary

Endobronchial ultrasound (EBUS) multimodal image including grey scale, blood flow doppler and elastography, can be used as non-invasive diagnosis and supplement the pathological result, which has important clinical application value. In this study, EBUS multimodal image database of 1000 inthoracic benign and malignant lymph nodes (LNs) will be constructed to train deep learning neural networks, which can automatically select representative images and diagnose LNs. Investigators will establish an artificial intelligence prediction model based on deep learning of intrathoracic LNs, and verify the model in other 300 LNs.

Conditions

  • Lymph Node Disease

Sponsors & Collaborators

  • Shanghai Chest Hospital

    lead OTHER

Principal Investigators

  • Jiayuan Sun, MD, PhD · Shanghai Chest Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-07-01
Primary Completion
2020-06-30
Completion
2020-12-31

Countries

  • China

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 NCT04328792 on ClinicalTrials.gov