Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiography

Pneumoconiosis Clinical Trial

Official title:

Investigate the Potential of Deep Learning in Assessing Pneumoconiosis Depicted on Digital Chest Radiographs and to Compare Its Performance With Certified Radiologists

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04963348
• Other study ID #: M2019467
• Status: Completed
• Start date: January 1, 2015
• Est. completion date: December 31, 2019

Study information

• Verified date: June 2021
• Source: Peking University Third Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical 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 two certified radiologists. The deep learning demonstrated a unique potential in classifying pneumoconiosis.

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).

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1881
• Est. completion date: December 31, 2019
• Est. primary completion date: December 31, 2018
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Pneumoconiosis

Intervention

Other:
• convolutional neural networks (CNNs)
CNN architecture named U-Net architecture

Locations

Country	Name	City	State
n/a

Sponsors (1)

Lead Sponsor	Collaborator
Peking University Third Hospital

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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).	up to 6 months