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Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays.

Blanca Priego-Torres1, Daniel Sanchez-Morillo1, Ebrahim Khalili1

  • 1Bioengineering, Automation and Robotics Research Group, Department of Automation Engineering, Electronics and Computer Architecture and Networks, School of Engineering, University of Cadiz, Puerto Real, 11519, Cádiz, Spain; Biomedical Research and Innovation Institute of Cadiz (INiBICA), Puerta del Mar University Hospital, Cádiz, 11009, Spain.

Computers in Biology and Medicine
|April 19, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning models show high accuracy in screening and staging silicosis from chest X-rays, aiding early occupational disease detection. These AI tools can improve diagnosis and patient outcomes by identifying lung disease early.

Keywords:
Chest X-rayDeep learningEngineered stoneProgressive massive fibrosis (PMF)SilicosisSimple silicosis (SS)

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Area of Science:

  • Occupational Health
  • Artificial Intelligence in Medicine
  • Radiology

Background:

  • Silicosis is a severe lung disease from silica dust exposure, posing a global health risk.
  • Engineered stone use increases silicosis risk; traditional diagnosis lacks sensitivity and has inter-observer variability.
  • Early silicosis detection is crucial for removing workers from occupational exposure.

Purpose of the Study:

  • To evaluate deep learning for automated silicosis screening and staging using chest X-rays.
  • To develop AI-driven clinical decision support tools for improved silicosis diagnosis.
  • To enhance the accuracy and effectiveness of occupational lung disease diagnostics.

Main Methods:

  • Utilized a dataset of chest X-rays from workers exposed to artificial quartz.
  • Implemented rib-cage segmentation for preprocessing.
  • Applied deep learning models for classification (screening and staging).

Main Results:

  • Segmentation model achieved high precision.
  • Screening models demonstrated near-perfect accuracy (ROC AUC 1.0).
  • Staging models achieved 81% accuracy and ROC AUC of 0.93, with challenges in differentiating simple silicosis from progressive massive fibrosis.

Conclusions:

  • Deep learning shows significant potential for accurate silicosis screening and staging.
  • AI tools can improve early diagnosis, enabling timely intervention and worker protection.
  • Further refinement is needed for complex cases like differentiating simple silicosis from progressive massive fibrosis.