Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Genome-wide association and MaODR-based multi-locus interaction analyses reveal a susceptibility gene network for newly identified metabolic syndrome.

Genome biology·2026
Same author

Effects of social media sponsorship disclosure on adolescents' advertising literacy and purchase intention.

PloS one·2026
Same author

The association of non-alcoholic fatty liver index, plasma metal levels, and genetic susceptibility using genome-wide type analysis.

Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS)·2026
Same author

Coptidis Rhizoma extract mitigates sleep deprivation-induced cognitive impairment and neurodegeneration: Insights into hormonal, glymphatic, and molecular mechanisms.

Journal of traditional and complementary medicine·2026
Same author

From hospital to home: National insights from Taiwan's hospital at home program.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases·2026
Same author

Is face shape associated with fitness of respiratory personal protective equipment-N95 masks?

Industrial health·2026

Related Experiment Video

Updated: Jan 11, 2026

A Standardized Method for Measuring Internal Lung Surface Area via Mouse Pneumonectomy and Prosthesis Implantation
08:46

A Standardized Method for Measuring Internal Lung Surface Area via Mouse Pneumonectomy and Prosthesis Implantation

Published on: July 26, 2017

13.8K

Combining MEA-Net and LAP-Net for Pneumoconiosis Staging Framework.

Yen-Yu Chen1, Hung-Yi Chuang2, Hsien-Chu Wu1

  • 1From the Department of Artificial Intelligence and Computer Engineering, National Chin-Yi University of Technology, Taichung, Taiwan (Y.-Y.C., H.-C.W.).

Journal of Occupational and Environmental Medicine
|November 17, 2025
PubMed
Summary

This study introduces a novel deep learning framework to enhance pneumoconiosis staging accuracy. The proposed method aids clinicians in more precise diagnosis of this hazardous occupational lung disease.

Keywords:
deep learningoccupational diseasepneumoconiosisstagingx-ray

More Related Videos

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

974
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.7K

Related Experiment Videos

Last Updated: Jan 11, 2026

A Standardized Method for Measuring Internal Lung Surface Area via Mouse Pneumonectomy and Prosthesis Implantation
08:46

A Standardized Method for Measuring Internal Lung Surface Area via Mouse Pneumonectomy and Prosthesis Implantation

Published on: July 26, 2017

13.8K
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

974
Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
08:05

Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia

Published on: December 19, 2020

14.7K

Area of Science:

  • Occupational Medicine
  • Radiology
  • Artificial Intelligence

Background:

  • Pneumoconiosis is a significant occupational hazard.
  • Current staging relies on subjective interpretation of lung X-rays by experienced physicians.
  • Individual clinical judgment introduces variability in diagnosis.

Purpose of the Study:

  • To develop an objective and accurate framework for pneumoconiosis staging.
  • To improve the diagnostic process by reducing subjectivity.
  • To leverage deep learning for enhanced classification of pneumoconiosis stages.

Main Methods:

  • A novel deep learning framework was proposed.
  • The framework integrates MEA-Net and LAP-Net architectures.
  • The method was evaluated for its effectiveness in pneumoconiosis staging.

Main Results:

  • The deep learning framework achieved high performance metrics.
  • Accuracy reached 95.24%, precision 95.15%, recall 95.15%, specificity 90.58%, F1-score 94.85%, and AUC 98.87% for four-stage classification.
  • These results demonstrate robust performance in classifying pneumoconiosis stages.

Conclusions:

  • The proposed deep learning framework offers a more accurate method for pneumoconiosis staging.
  • This AI-driven approach can assist physicians in diagnosing pneumoconiosis stages.
  • The study highlights the potential of AI in improving occupational disease diagnosis.