A deep learning algorithm that aids visualization of femoral neck fractures and improves physician training.

Journal: Injury
PMID: 39504732

Abstract

PURPOSE: Missed fractures are the most common radiologic error in clinical practice, and erroneous classification could lead to inappropriate treatment and unfavorable prognosis. Here, we developed a fully automated deep learning model to detect and classify femoral neck fractures using plain radiographs, and evaluated its utility for diagnostic assistance and physician training.

Authors

  • Pengyi Xing
    Department of Radiology, Changhai Hospital, Shanghai, China. Electronic address: 746992685@qq.com.
  • Li Zhang
    Department of Animal Nutrition and Feed Science, College of Animal Science and Technology, Huazhong Agricultural University, Wuhan 430070, China.
  • Tiegong Wang
    Department of Radiology, Changhai Hospital.
  • Lipeng Wang
    College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
  • Wanting Xing
    Department of Radiology, The 989th Hospital of the PLA Joint Logistics Support Force, Luoyang, Henan Province, China.
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.

Keywords

External Resources

Popular Topics
Recent Journals