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NCT04963348

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

Completed Last updated 15 July 2021
What this trial tests

trial testing convolutional neural networks (CNNs) in Pneumoconiosis in 1,881 participants. Completed in 31 December 2019.

Timeline
1 January 2015
Primary endpoint
31 December 2018
31 December 2019

Quick facts

Lead sponsorPeking University Third Hospital
StatusCompleted
Study typeOBSERVATIONAL
Enrollment1,881
Start date1 January 2015
Primary completion31 December 2018
Estimated completion31 December 2019

Drugs / interventions tested

Conditions studied

Sponsor

Peking University Third Hospital

Who can join

Eligibility, any sex, with Pneumoconiosis. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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.

Publications & conference data

No peer-reviewed publications indexed yet for this trial. Completed trials usually publish results within 12-18 months.

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Other recruiting trials for Pneumoconiosis

Currently open trials in the same condition.

Other Peking University Third Hospital trials

Trials by the same sponsor.

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Data sources for this page

Drug Landscape aggregates and links these public records for informational use only. Always verify against the primary source before clinical or regulatory decisions. Canonical URL: https://druglandscape.com/trial/NCT04963348.

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