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Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiography

NCT04963348 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1881

Last updated 2021-07-15

No results posted yet for this study

Summary

Pneumoconiosis is relatively prevalent in low/middle-income countries, and it remains a challenging task to accurately and reliably diagnose pneumoconiosis. The investigators implemented a deep learning solution and clarified the potential of deep learning in pneumoconiosis diagnosis by comparing its performance with two certified radiologists. The deep learning demonstrated a unique potential in classifying pneumoconiosis.

Conditions

  • Pneumoconiosis

Interventions

OTHER

convolutional neural networks (CNNs)

CNN architecture named U-Net architecture

Sponsors & Collaborators

  • Peking University Third Hospital

    lead OTHER

Principal Investigators

  • Xiaohua Wang · Peking University Third Hospital

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2015-01-01
Primary Completion
2018-12-31
Completion
2019-12-31

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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 NCT04963348 on ClinicalTrials.gov