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Evaluation of Pneumoconiosis High Risk Early Warning Models

NCT04952675 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 200

Last updated 2021-07-07

No results posted yet for this study

Summary

Precaution of pneumoconiosis is more important than treatment. However, the current process can't early warn the high-risk dust exposed workers until they are diagnosed with pneumoconiosis. With the feature of efficiency, impersonality and quantification, artificial intelligence is just appropriate for solving this problems. Therefore, we are aiming at adapting deep learning to develop models of pneumoconiosis intelligent detection, grade diagnosis and high risk early warning. The annotated images will be used for convolutional neural networks (CNNs) algorithm training, aiming at pneumoconiosis screening and grade diagnosis. Moreover, risk score calculated by density heat map will be used for early warning of dust-exposed workers. Then follow up of cohort will be implied to verify the validity of the risk score. By this way, the high-risk dust-exposed workers will get early intervention and better prognosis, which can obviously reduce medical burden.

Conditions

  • Pneumoconiosis

Sponsors & Collaborators

  • Peking University Third Hospital

    lead OTHER

Principal Investigators

  • Xiao Li, M.D. · Peking University Third Hospital

Eligibility

Min Age
18 Years
Max Age
60 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-08-01
Primary Completion
2021-12-31
Completion
2025-12-31

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 NCT04952675 on ClinicalTrials.gov