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Evaluation of Pneumoconiosis High Risk Early Warning Models

Study Purpose

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.

Recruitment Criteria

Accepts Healthy Volunteers

Healthy volunteers are participants who do not have a disease or condition, or related conditions or symptoms

No
Study Type

An interventional clinical study is where participants are assigned to receive one or more interventions (or no intervention) so that researchers can evaluate the effects of the interventions on biomedical or health-related outcomes.


An observational clinical study is where participants identified as belonging to study groups are assessed for biomedical or health outcomes.


Searching Both is inclusive of interventional and observational studies.

Observational
Eligible Ages 18 Years - 60 Years
Gender All
More 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

Trial Details

Trial ID:

This trial id was obtained from ClinicalTrials.gov, a service of the U.S. National Institutes of Health, providing information on publicly and privately supported clinical studies of human participants with locations in all 50 States and in 196 countries.

NCT04952675
Phase

Phase 1: Studies that emphasize safety and how the drug is metabolized and excreted in humans.

Phase 2: Studies that gather preliminary data on effectiveness (whether the drug works in people who have a certain disease or condition) and additional safety data.

Phase 3: Studies that gather more information about safety and effectiveness by studying different populations and different dosages and by using the drug in combination with other drugs.

Phase 4: Studies occurring after FDA has approved a drug for marketing, efficacy, or optimal use.

Lead Sponsor

The sponsor is the organization or person who oversees the clinical study and is responsible for analyzing the study data.

Peking University Third Hospital
Principal Investigator

The person who is responsible for the scientific and technical direction of the entire clinical study.

Xiao Li, M.D.
Principal Investigator Affiliation Peking University Third Hospital
Agency Class

Category of organization(s) involved as sponsor (and collaborator) supporting the trial.

Other
Overall Status Recruiting
Countries China
Conditions

The disease, disorder, syndrome, illness, or injury that is being studied.

Pneumoconiosis
Additional Details

Pneumoconiosis, the predominant occupational disease in China and all over the world. Chest radiography is the most accessible and affordable radiological test available for the physical examination of dust-exposed workers and mass screening for pneumoconiosis. But the diagnosis process has some disadvantages, such as strong subjectivity, inefficiency, and disability of judgement of borderline lesion, etc. Besides, 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 the aforesaid problems. Up to now, there has been rare research about adapting deep learning for pneumoconiosis grade diagnosis and high risk early warning. In our previous studies, we set up a chest radiograph database, which contains more than 100,000 digital pneumoconiosis radiography images. The result of detection-system evaluation demonstrated that the accuracy in the identification of pneumoconiosis could reach 90%, with an AUC(Area Under The Curve) of 0.965 and a sensitivity of 99%. More works need to be continued. 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.

Arms & Interventions

Arms

: low-risk group

Risk Index∈[0,0.5)

: high-risk group

Risk Index∈[0.5,1)

Interventions

Contact a Trial Team

If you are interested in learning more about this trial, find the trial site nearest to your location and contact the site coordinator via email or phone. We also strongly recommend that you consult with your healthcare provider about the trials that may interest you and refer to our terms of service below.

International Sites

Peking University Third Hospital, Beijing, Beijing, China

Status

Recruiting

Address

Peking University Third Hospital

Beijing, Beijing, 100191

Site Contact

Xiao Li, M.D.

lixiao.sy@bjmu.edu.cn

+8613051709411