Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiography
Study Details
Study Description
Brief 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 two certified radiologists. The deep learning demonstrated a unique potential in classifying pneumoconiosis.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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
Arms and Interventions
| Arm | Intervention/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 |
Other: convolutional neural networks (CNNs)
CNN architecture named U-Net architecture
Other Names:
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Outcome Measures
Primary Outcome Measures
- the diagnosis of pneumoconiosis [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).
Eligibility Criteria
Criteria
Inclusion Criteria:
- industrial workers with a history of exposure to dust and underwent DR screening of pneumoconiosis from 2015 to 2018
Exclusion Criteria:
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patients with poor image quality
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patients with incomplete clinical data
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Peking University Third Hospital
Investigators
- Study Chair: Xiaohua Wang, Peking University Third Hospital
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- M2019467