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Prediction Model of Peripheral Pulmonary Lesions Based on R-EBUS Image

NCT04497233 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1300

Last updated 2020-08-04

No results posted yet for this study

Summary

Peripheral pulmonary lesions(PPLs) have a wide spectrum of diseases, and the diagnosis will affect the treatment strategy and prognosis. Radial endobronchial ultrasound (R-EBUS) can be used for non-invasive diagnosis of PPLs, and the supplement pathological diagnosis results of EBUS-TBLB, which has important clinical application value. This project intends to select representative images from R-EBUS dynamic videos for qualitative and quantitative analysis, to establish and verify the diagnostic evaluation system of R-EBUS forPPLs. Then build 1,000 R-EBUS image databases of PPLs, train deep learning networks for automatic extraction and diagnosis of target areas, and automatically extract representative images from videos to establish a benign and malignant prediction model of PPLs. We will provide reliable theoretical basis for the diagnosis of PPLs, and optimize the diagnosis and treatment method.The network would be prospectively verified through 300 R-EBUS images from multi centers.

Conditions

  • Diagnoses Disease

Sponsors & Collaborators

  • Shanghai Chest Hospital

    lead OTHER

Principal Investigators

  • Jiayuan Sun, PhD · Shanghai Chest Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-07-01
Primary Completion
2020-09-30
Completion
2021-03-31

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 NCT04497233 on ClinicalTrials.gov