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A deep learning-based model for screening and staging pneumoconiosis.

Liuzhuo Zhang1,2, Ruichen Rong2, Qiwei Li3

  • 1Shenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen, Guangdong, China.

Scientific Reports
|January 27, 2021
PubMed
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An artificial intelligence (AI) model aids radiologists in detecting and staging pneumoconiosis from chest X-rays. This AI tool demonstrated high accuracy in screening and outperformed human radiologists in staging the occupational lung disease.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Occupational Health

Background:

  • Pneumoconiosis is a serious occupational lung disease requiring accurate screening and staging.
  • Radiographic interpretation for pneumoconiosis can be challenging and subjective.
  • AI offers potential for improving diagnostic accuracy and efficiency in medical imaging.

Purpose of the Study:

  • To develop and validate an AI-based model for pneumoconiosis screening and staging using chest radiographs.
  • To compare the AI model's performance against human radiologists.

Main Methods:

  • Development of a deep learning model using a training cohort of chest radiographs.
  • Lung field segmentation into six subregions.
  • Convolutional Neural Network (CNN) classification for opacity prediction in each subregion.

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  • Consolidation of subregion predictions for final diagnosis (normal, stage I-III pneumoconiosis).
  • Validation on an independent test cohort with radiologist-annotated labels.

    • Main Results:

    • The AI model achieved a pneumoconiosis screening accuracy of 0.973, with sensitivity and specificity exceeding 0.97.
    • Pneumoconiosis staging accuracy was 0.927, surpassing the performance of two radiologist groups (0.87 and 0.84).

    Conclusions:

    • A deep learning model can effectively screen and stage pneumoconiosis from chest radiographs.
    • The AI model demonstrates superior performance in pneumoconiosis staging compared to human radiologists.
    • AI-assisted radiography shows promise for screening and diagnosing occupational lung diseases.