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

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.

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Key information

Conditions

Pneumoconiosis Lung Diseases Lung Diseases, Interstitial Lung Injury Occupational Diseases Respiratory Tract Diseases

Sex eligibility

All sexes

Study type

Observational

About this study

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).

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

convolutional neural networks (CNNs)

Other

CNN architecture named U-Net architecture

Other names: deep learning technology

Primary outcomes

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).

Sponsors and collaborators

Lead sponsor

Peking University Third Hospital

Other

Registry information

Official study title

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

Important dates

Study start: 2015
Primary completion: 2018
Study completion: 2019
First posted: Jul 15, 2021
Registry last updated: Jul 15, 2021

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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