Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics.

Journal: BMC medical imaging
Published Date: Jul 1, 2025

Abstract

BACKGROUND: In clinical work, there are difficulties in distinguishing pulmonary contusion(PC) from bacterial pneumonia(BP) on CT images by the naked eye alone when the history of trauma is unknown. Artificial intelligence is widely used in medical imaging, but its diagnostic performance for pulmonary contusion is unclear. In this study, artificial intelligence was used for the first time to identify lung contusion and bacterial pneumonia, and its diagnostic performance was compared with that of manual.

Authors

  • Tie Deng
    Medical Imaging Department, Chongqing Emergency Medical Center, School of Medicine, Chongqing University Central Hospital, Chongqing University, No. 1, Jiankang Road, Yuzhong District, Chongqing, 400014, China.
  • Junbang Feng
    Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China.
  • Xingyan Le
    Medical Imaging Department, Chongqing Emergency Medical Center, Chongqing University Central Hospital, School of Medicine, Chongqing University, Chongqing, China.
  • Yuwei Xia
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • Feng Shi
    Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China.
  • Fei Yu
    Department of Nutrition and food hygiene, College of Public Health of Zhengzhou University, Zhengzhou, China, 450001. Electronic address: 53615631@qq.com.
  • Yiqiang Zhan
  • Xinghua Liu
    School of Electrical Engineering, Xi'an University of Technology, Xi'an 710048, China.
  • Chuanming Li
    Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China.

Keywords

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