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Silicodata: An Annotated Benchmark CXR Dataset for Silicosis Detection.

Yasmeena Akhter1, Rishabh Ranjan1, Mayank Vatsa1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology Jodhpur, Jodhpur, 342030, India.

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|September 26, 2025
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Summary

This study introduces Silicodata, a novel dataset for Silicosis detection. Current AI models show limited accuracy, highlighting the need for this resource in developing automated diagnostic tools for this occupational lung disease.

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Area of Science:

  • Occupational Medicine
  • Radiology
  • Artificial Intelligence in Healthcare

Background:

  • Silicosis is a severe global occupational lung disease within the Pneumoconiosis family.
  • Healthcare data collection and expert annotation for rare diseases like Silicosis present significant challenges.
  • Diagnostic complexity arises from overlapping symptoms with conditions such as tuberculosis and silicotuberculosis.

Purpose of the Study:

  • To introduce the first public, comprehensively annotated dataset for Silicosis and related lung diseases.
  • To facilitate the development and validation of AI algorithms for medical applications in occupational lung disease detection.
  • To address the common challenge of small sample sizes in medical AI research.

Main Methods:

  • Creation of a unique dataset, Silicodata, containing images of Silicosis, tuberculosis, silicotuberculosis, and healthy lungs.
  • Detailed annotations for lung and disease region segmentation and disease prediction by multiple expert radiologists.
  • Baseline experiments utilizing current AI models to evaluate predictive accuracy.

Main Results:

  • Current AI models demonstrate limited predictive accuracy for Silicosis and related disease classes within the dataset.
  • The dataset provides a valuable resource for training and testing AI models, particularly for conditions with overlapping symptoms.
  • Initial findings underscore the necessity for further dedicated research and model development.

Conclusions:

  • The proposed Silicodata dataset is crucial for advancing automated Silicosis detection tools.
  • This resource can significantly aid in overcoming the limitations of small sample sizes in medical AI research for occupational lung diseases.
  • Further research is essential to improve AI model performance in diagnosing complex lung conditions.