Deep Learning for Automated Classification of Hip Hardware on Radiographs.

Journal: Journal of imaging informatics in medicine
PMID: 39266912

Abstract

PURPOSE: To develop a deep learning model for automated classification of orthopedic hardware on pelvic and hip radiographs, which can be clinically implemented to decrease radiologist workload and improve consistency among radiology reports.

Authors

  • Yuntong Ma
    State Key Laboratory of Characteristic Chinese Medicine Resources in Southwest China, Chengdu University of Traditional Chinese Medicine, Chengdu, 611137, China.
  • Justin L Bauer
    Department of Radiology, Stanford Medicine. 300 Pasteur Dr, Palo Alto, CA, 94304, USA.
  • Acacia H Yoon
    Menlo-Atherton High School, 555 Middlefield Road Atherton, Atherton, CA, 94027, USA.
  • Christopher F Beaulieu
    Department of Radiology, Stanford University, Stanford, California, United States of America.
  • Luke Yoon
    Department of Radiology, Stanford Medicine. 300 Pasteur Dr, Palo Alto, CA, 94304, USA.
  • Bao H Do
    Department of Radiology, Stanford University School of Medicine, 300 Pasteur Drive, H3630, Stanford, CA, 94305.
  • Charles X Fang
    Department of Radiology, Stanford Medicine. 300 Pasteur Dr, Palo Alto, CA, 94304, USA. cxfang@stanford.edu.

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