Detecting hip osteoarthritis on clinical CT: a deep learning application based on 2-D summation images derived from CT.

Journal: Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
PMID: 34476540

Abstract

UNLABELLED: We developed and compared deep learning models to detect hip osteoarthritis on clinical CT. The CT-based summation images, CT-AP, that resemble X-ray radiographs can detect radiographic hip osteoarthritis and in the absence of large training data, a reliable deep learning model can be optimized by combining CT-AP and X-ray images.

Authors

  • R K Gebre
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland. robel.gebre@oulu.fi.
  • J Hirvasniemi
    Center for Machine Vision and Signal Analysis, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland; Department of Orthopedics, University Medical Center Utrecht, Utrecht, the Netherlands. Electronic address: jukka.hirvasniemi@oulu.fi.
  • R A van der Heijden
    Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, the Netherlands.
  • I Lantto
    Division of Orthopaedic and Trauma Surgery, Oulu University Hospital, Oulu, Finland.
  • S Saarakkala
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland. Electronic address: simo.saarakkala@oulu.fi.
  • J Leppilahti
    Division of Orthopaedic and Trauma Surgery, Oulu University Hospital, Oulu, Finland.
  • T Jämsä
    Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Oulu, Finland.

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