Early warning models for pneumoconiosis risk in dust-exposed workers
The Development and Clinical Application of Pneumoconiosis High Risk Early Warning Models Based on Convolutional Neural Network in Chest Radiography
This study is testing a new way to use artificial intelligence to spot early signs of pneumoconiosis in workers exposed to dust, helping to identify those at risk so they can get timely care.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 200 (estimated) |
| Ages | 18 Years to 60 Years |
| Sex | All |
| Sponsor | Peking University Third Hospital Academic / other |
| Locations | 1 site (Beijing, Beijing) |
| Trial ID | NCT04952675 on ClinicalTrials.gov |
What this trial studies
This study focuses on developing artificial intelligence models to detect pneumoconiosis and assess the risk in workers exposed to dust. By utilizing deep convolutional neural networks (CNNs) trained on a large database of chest radiographs, the study aims to improve early diagnosis and risk assessment for pneumoconiosis. The models will provide a risk score based on density heat maps, allowing for timely interventions for high-risk individuals. The ultimate goal is to reduce the medical burden associated with pneumoconiosis through early warning and better prognosis.
Who should consider this trial
Good fit: Ideal candidates for this study are workers who have been exposed to dust and have undergone digital chest radiography.
Not a fit: Patients with existing pulmonary diseases or those who have left dust-exposed work may not benefit from this study.
Why it matters
Potential benefit: If successful, this approach could lead to earlier detection and intervention for pneumoconiosis, significantly improving patient outcomes.
How similar studies have performed: While there has been limited research on using deep learning for pneumoconiosis detection, previous studies have shown promising accuracy in identifying the disease.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: 1. workers exposed to dust; 2. have digital chest radiography Exclusion Criteria: 1. basal pulmonary disease; 2. dimission from dust-exposed work
Where this trial is running
Beijing, Beijing
- Peking University Third Hospital — Beijing, Beijing, China (Recruiting)
Study contacts
- Principal investigator: Xiao Li, M.D. — Peking University Third Hospital
- Study coordinator: Xiao Li, M.D.
- Email: lixiao.sy@bjmu.edu.cn
- Phone: +8613051709411
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.