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

July 8, 2021 updated by: Peking University Third Hospital

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

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 Type

Observational

Enrollment (Actual)

1881

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Of these subjects, 923 were diagnosed with pneumoconiosis, 958 were normal. Among these subjects, 163 were females.

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Case-Only
  • Time Perspectives: Retrospective

Cohorts and Interventions

Group / Cohort
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:
  • deep learning technology

What is the study measuring?

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

This is where you will find people and organizations involved with this study.

Sponsor

Peking University Third Hospital

Investigators

  • Study Chair: Xiaohua Wang, Peking University Third Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2015

Primary Completion (Actual)

December 31, 2018

Study Completion (Actual)

December 31, 2019

Study Registration Dates

First Submitted

June 28, 2021

First Submitted That Met QC Criteria

July 8, 2021

First Posted (Actual)

July 15, 2021

Study Record Updates

Last Update Posted (Actual)

July 15, 2021

Last Update Submitted That Met QC Criteria

July 8, 2021

Last Verified

June 1, 2021

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • M2019467

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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