Evaluation of Pneumoconiosis High Risk Early Warning Models

Pneumoconiosis Clinical Trial

Official title:

The Development and Clinical Application of Pneumoconiosis High Risk Early Warning Models Based on Convolutional Neural Network in Chest Radiography

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04952675
• Other study ID #: PekingUTH-002
• Status: Recruiting
• Start date: August 1, 2018
• Est. completion date: December 2025

Study information

• Verified date: June 2021
• Source: Peking University Third Hospital
• Contact: Xiao Li, M.D.
• Phone: +8613051709411
• Email: lixiao.sy[@]bjmu.edu.cn
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 200
• Est. completion date: December 2025
• Est. primary completion date: December 2021
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 60 Years

Eligibility

Inclusion Criteria:

1. workers exposed to dust;

2. have digital chest radiography

Exclusion Criteria:

1. basal pulmonary disease;

2. dimission from dust-exposed work

Related Conditions & MeSH terms

• Pneumoconiosis

Locations

Country	Name	City	State
China	Peking University Third Hospital	Beijing	Beijing

Sponsors (1)

Lead Sponsor	Collaborator
Peking University Third Hospital

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	participants diagnosed as "pneumoconiosis"	Number of Participants diagnosed as "pneumoconiosis"	before December, 31,2022
Primary	death	Number of Participants who dies	before December, 31,2022
Secondary	Forced Expiratory Volume In 1s(FEV1) in %	Forced Expiratory Volume In 1s	before December, 31,2022
Secondary	arterial partial pressure of oxygen, PaO2	arterial partial pressure of oxygen	before December, 31,2022
Secondary	modified Medical Research Council,mMRC	a questionnaire used to assess symptom	before December, 31,2022