Evaluation of Pneumoconiosis High Risk Early Warning Models
The Development and Clinical Application of Pneumoconiosis High Risk Early Warning Models Based on Convolutional Neural Network in Chest Radiography
1 other identifier
observational
200
1 country
1
Brief 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Aug 2018
Longer than P75 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
August 1, 2018
CompletedFirst Submitted
Initial submission to the registry
June 23, 2021
CompletedFirst Posted
Study publicly available on registry
July 7, 2021
CompletedPrimary Completion
Last participant's last visit for primary outcome
December 1, 2021
CompletedStudy Completion
Last participant's last visit for all outcomes
December 1, 2025
CompletedJuly 7, 2021
June 1, 2021
3.3 years
June 23, 2021
July 2, 2021
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
participants diagnosed as "pneumoconiosis"
Number of Participants diagnosed as "pneumoconiosis"
before December, 31,2022
death
Number of Participants who dies
before December, 31,2022
Secondary Outcomes (3)
Forced Expiratory Volume In 1s(FEV1) in %
before December, 31,2022
arterial partial pressure of oxygen, PaO2
before December, 31,2022
modified Medical Research Council，mMRC
before December, 31,2022
Study Arms (2)
low-risk group
Risk Index∈\[0,0.5)
high-risk group
Risk Index∈\[0.5,1)
Eligibility Criteria
dust-exposed workers of 16 provinces of China
You may qualify if:
- workers exposed to dust;
- have digital chest radiography
You may not qualify if:
- basal pulmonary disease;
- dimission from dust-exposed work
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Peking University Third Hospital
Beijing, Beijing Municipality, 100191, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Xiao Li, M.D.
Peking University Third Hospital
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
June 23, 2021
First Posted
July 7, 2021
Study Start
August 1, 2018
Primary Completion
December 1, 2021
Study Completion
December 1, 2025
Last Updated
July 7, 2021
Record last verified: 2021-06