PneumoconiosisNCT04963348
Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiography
An observational study of Pneumoconiosis, run by Peking University Third Hospital.
Trial summary
Pneumoconiosis is relatively prevalent in low/middle-income countries, and it remains a challenging task to accurately and reliably diagnose pneumoconiosis. The investigators implemented a deep learning solution and clarified the potential of deep learning in pneumoconiosis diagnosis by comparing its performance with… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Pneumoconiosis
- What is being tested?
- convolutional neural networks (CNNs)
- How many people
- 1,881 participants
- Where
- No sites listed yet
- Study dates
- 1 Jan 2015to 31 Dec 2019
- 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Of these subjects, 923 were diagnosed with pneumoconiosis, 958 were normal. Among these subjects, 163 were females.
Who may be able to join
- industrial workers with a history of exposure to dust and underwent DR screening of pneumoconiosis from 2015 to 2018
Who cannot join
- patients with poor image quality
- patients with incomplete clinical data
About this study
Official title: Investigate the Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiographs and to Compare Its Performance With Certified Radiologists
Pneumoconiosis is relatively prevalent in low/middle-income countries, and it remains a challenging task to accurately and reliably diagnose pneumoconiosis. The investigators implemented a deep learning solution and clarified the potential of deep learning in pneumoconiosis diagnosis by comparing its performance with two certified radiologists. The deep learning demonstrated a unique potential in classifying pneumoconiosis.
Read the detailed description
The investigators retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, the investigators applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC).
- Conditions
- Pneumoconiosis
- Being tested
- convolutional neural networks (CNNs)
- Medical subject headings
- Pneumoconiosis
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
convolutional neural networks (CNNs)Other
Also called deep learning technology
CNN architecture named U-Net architecture
| Study group | Role | What they receive |
|---|---|---|
| convolutional neural network (CNN) | a classical deep convolutional neural network (CNN) called Inception-V3 was applied to the image sets and validated the classification performance of the trained models Receives: Other: convolutional neural networks (CNNs) |
What is being measured
The primary outcome is the main question the study is designed to answer.
the diagnosis of pneumoconiosis
The diagnosis and staging of pneumoconiosis were made by an expert panel consisting of certified radiologists and occupational physicians. The diagnosis of pneumoconiosis was confirmed by medical history and previous medical records(chest X-rays and pulmonary function testing).
Measured: up to 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jul 2021
- Study start1 Jan 2015
- Primary completion31 Dec 2018
- Study completion31 Dec 2019
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
- Case only
- Time perspective
- Retrospective
- Enrolment
- 1,881 participants (actual)
Sponsor and investigators
- Lead sponsor
- Peking University Third Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Xiaohua Wang, Study chair, Peking University Third Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04963348
- Sponsor's protocol ID
- M2019467
- Status last verified
- Jun 2021
- First submitted
- 28 Jun 2021
- Last updated
- 15 Jul 2021
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 15 Jul 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Investigate the Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiographs and to Compare Its Performance With Certified Radiologists
- ClinicalTrials.gov ID
- NCT04963348
- Acronym / sponsor ID
- M2019467
- Conditions
- Pneumoconiosis
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,881 participants (actual)
- Lead sponsor
- Peking University Third HospitalUniversity, hospital or other
- Study start
- 1 Jan 2015 (actual)
- Primary completion
- 31 Dec 2018 (actual)
- Study completion
- 31 Dec 2019 (actual)
- First posted
- 15 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.
Pneumoconiosis is relatively prevalent in low/middle-income countries, and it remains a challenging task to accurately and reliably diagnose pneumoconiosis. The investigators implemented a deep learning solution and clarified the potential of deep learning in pneumoconiosis diagnosis by comparing its performance with two certified radiologists. The deep learning demonstrated a unique potential in classifying pneumoconiosis.
Detailed description
The investigators retrospectively collected a dataset consisting of 1881 chest X-ray images in the form of digital radiography. These images were acquired in a screening setting on subjects who had a history of working in an environment that exposed them to harmful dust. Among these subjects, 923 were diagnosed with pneumoconiosis, and 958 were normal. To identify the subjects with pneumoconiosis, the investigators applied a classical deep convolutional neural network (CNN) called Inception-V3 to these image sets and validated the classification performance of the trained models using the area under the receiver operating characteristic curve (AUC).
Study design
- Observational model
- Case only
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| convolutional neural network (CNN) | – | Other: convolutional neural networks (CNNs)a classical deep convolutional neural network (CNN) called Inception-V3 was applied to the image sets and validated the classification performance of the trained models |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Of these subjects, 923 were diagnosed with pneumoconiosis, 958 were normal. Among these subjects, 163 were females.
Key inclusion criteria (1 listed)
- industrial workers with a history of exposure to dust and underwent DR screening of pneumoconiosis from 2015 to 2018
Key exclusion criteria (2 listed)
- patients with poor image quality
- patients with incomplete clinical data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| convolutional neural networks (CNNs)Also: deep learning technologyCNN architecture named U-Net architecture | Other | convolutional neural network (CNN) |
Primary outcome
the diagnosis of pneumoconiosis
Time frame: up to 6 months
The diagnosis and staging of pneumoconiosis were made by an expert panel consisting of certified radiologists and occupational physicians. The diagnosis of pneumoconiosis was confirmed by medical history and previous medical records(chest X-rays and pulmonary function testing).
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jul 2021.
- Individual participant dataSharing planned: No
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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.
Xiaohua Wang study chair
Peking University Third Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Pneumoconiosis, run by Peking University Third Hospital.
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 1 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: finished, as of the sponsor's last update on 15 Jul 2021. Status can change between updates, so confirm with the study contact before making plans.
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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
the diagnosis of pneumoconiosis (time frame: up to 6 months). 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 15 Jul 2021.