PneumoconiosisNCT04952675
Evaluation of Pneumoconiosis High Risk Early Warning Models
An observational study of Pneumoconiosis, run by Peking University Third Hospital at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 18 to 60 years
- Conditions the study is for
- Pneumoconiosis
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2018to Dec 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 60 years
- All sexes
- No healthy volunteers
- Adult
Study population: dust-exposed workers of 16 provinces of China
Who may be able to join
- workers exposed to dust;
- have digital chest radiography
Who cannot join
- basal pulmonary disease;
- dimission from dust-exposed work
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Peking University Third Hospital
Beijing, Beijing Municipality, 100191
Xiao Li, M.D., +8613051709411, lixiao.sy@bjmu.edu.cn
About this study
Official title: The Development and Clinical Application of Pneumoconiosis High Risk Early Warning Models Based on Convolutional Neural Network in Chest Radiography
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.
Read the 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
- Keywords
- artificial intelligence, pneumoconiosis, Deep Convolutional Neural Networks, computer-aided diagnosis
- Medical subject headings
- Pneumoconiosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| low-risk group | Risk Index∈[0,0.5) | |
| high-risk group | Risk Index∈[0.5,1) |
What is being measured
The primary outcome is the main question the study is designed to answer.
participants diagnosed as "pneumoconiosis"
Number of Participants diagnosed as "pneumoconiosis"
Measured: before December, 31,2022
death
Number of Participants who dies
Measured: before December, 31,2022
Secondary outcomes (3)
Forced Expiratory Volume In 1s(FEV1) in %
Forced Expiratory Volume In 1s
Measured: before December, 31,2022
arterial partial pressure of oxygen, PaO2
arterial partial pressure of oxygen
Measured: before December, 31,2022
modified Medical Research Council，mMRC
a questionnaire used to assess symptom
Measured: before December, 31,2022
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jul 2021
- Study start1 Aug 2018
- Primary completionDec 2021 (estimated)
- Study completionDec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PneumoconiosisStudies of the same condition, open and under way.
- More trials run by Peking University Third HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Xiao Li, M.D., Principal investigator, Peking University Third Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04952675
- Sponsor's protocol ID
- PekingUTH-002
- Status last verified
- Jun 2021
- First submitted
- 23 Jun 2021
- Last updated
- 7 Jul 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 7 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Development and Clinical Application of Pneumoconiosis High Risk Early Warning Models Based on Convolutional Neural Network in Chest Radiography
- ClinicalTrials.gov ID
- NCT04952675
- Acronym / sponsor ID
- PekingUTH-002
- Conditions
- Pneumoconiosis
- Keywords
- artificial intelligence, pneumoconiosis, Deep Convolutional Neural Networks, computer-aided diagnosis
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Study start
- 1 Aug 2018 (actual)
- Primary completion
- Dec 2021 (estimated)
- Study completion
- Dec 2025 (estimated)
- First posted
- 7 Jul 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| low-risk group | – | –Risk Index∈[0,0.5) |
| high-risk group | – | –Risk Index∈[0.5,1) |
Who may qualify
- Ages
- 18 to 60 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- Study population
- dust-exposed workers of 16 provinces of China
Key inclusion criteria (2 listed)
- workers exposed to dust;
- have digital chest radiography
Key exclusion criteria (2 listed)
- basal pulmonary disease;
- dimission from dust-exposed work
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
participants diagnosed as "pneumoconiosis"
Time frame: before December, 31,2022
Number of Participants diagnosed as "pneumoconiosis"
death
Time frame: before December, 31,2022
Number of Participants who dies
3 secondary outcomes
Forced Expiratory Volume In 1s(FEV1) in %
Time frame: before December, 31,2022
arterial partial pressure of oxygen, PaO2
Time frame: before December, 31,2022
modified Medical Research Council，mMRC
Time frame: before December, 31,2022
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Jul 2021.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Xiao Li, M.D. principal investigator
Peking University Third Hospital
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Pneumoconiosis, run by Peking University Third Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 7 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Xiao Li, M.D. (+8613051709411, lixiao.sy@bjmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
participants diagnosed as "pneumoconiosis" (time frame: before December, 31,2022). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Jul 2021.