Deep learning-based algorithm for classifying high-resolution computed tomography features in coal workers' pneumoconiosis.

Journal: Biomedical engineering online
PMID: 39871257

Abstract

BACKGROUND: Coal workers' pneumoconiosis is a chronic occupational lung disease with considerable pulmonary complications, including irreversible lung diseases that are too complex to accurately identify via chest X-rays. The classification of clinical imaging features from high-resolution computed tomography might become a powerful clinical tool for diagnosing pneumoconiosis in the future.

Authors

  • Hantian Dong
    The First College for Clinical Medicine, Shanxi Medical University, No. 56 Xinjian South Road, Taiyuan, 030001, Shanxi, People's Republic of China.
  • Biaokai Zhu
    Network Security Department, Shanxi Police College, No. 799 Qingdong Road, Qingxu Country, Taiyuan, 030021, Shanxi, People's Republic of China.
  • Xiaomei Kong
    National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Respiratory and Critical Care Medicine, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China.
  • Xuesen Su
    The First College for Clinical Medicine, Shanxi Medical University, No. 56 Xinjian South Road, Taiyuan, 030001, Shanxi, People's Republic of China.
  • Ting Liu
    School of Public Health, Shanxi Medical University, Taiyuan 030000, China.
  • Xinri Zhang
    National Health Commission Key Laboratory of Pneumoconiosis, Shanxi Key Laboratory of Respiratory Diseases, Department of Respiratory and Critical Care Medicine, First Hospital of Shanxi Medical University, No. 85 Jiefang South Road, Taiyuan, 030001, Shanxi, People's Republic of China. XinriZhang@outlook.com.

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