Evaluation of Pneumoconiosis High Risk Early Warning Models

NCT ID: NCT04952675

Last Updated: 2021-07-07

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 200 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2018-08-01
• Study Completion Date: 2025-12-31

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.

Detailed Description

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.

Conditions

Pneumoconiosis

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

low-risk group

Risk Index∈\[0,0.5)

No interventions assigned to this group

high-risk group

Risk Index∈\[0.5,1)

No interventions assigned to this group

Eligibility 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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 60 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Peking University Third Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Xiao Li, M.D.

Role: PRINCIPAL_INVESTIGATOR

Peking University Third Hospital

Locations

Peking University Third Hospital

Beijing, Beijing Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Xiao Li, M.D.

Role: CONTACT

lixiao.sy@bjmu.edu.cn

+8613051709411

Facility Contacts

Xiao Li, M.D.

Role: primary

lixiao.sy@bjmu.edu.cn

+8613051709411

Other Identifiers

PekingUTH-002

Identifier Type: -

Identifier Source: org_study_id