Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiography
Investigate the Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiographs and to Compare Its Performance With Certified Radiologists
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- industrial workers with a history of exposure to dust and underwent DR screening of pneumoconiosis from 2015 to 2018
Exclusion Criteria:
- patients with poor image quality
- patients with incomplete clinical data
Study Plan
How is the study designed?
Design Details
- Observational Models: Case-Only
- Time Perspectives: Retrospective
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
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
|
CNN architecture named U-Net architecture
Other Names:
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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).
|
up to 6 months
|
Collaborators and Investigators
Sponsor
Sponsor
Investigators
Investigators
- Study Chair: Xiaohua Wang, Peking University Third Hospital
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- M2019467
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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