Application of deep learning for automated diagnosis and classification of hip dysplasia on plain radiographs.

Journal: BMC musculoskeletal disorders
PMID: 38336666

Abstract

BACKGROUND: Hip dysplasia is a condition where the acetabulum is too shallow to support the femoral head and is commonly considered a risk factor for hip osteoarthritis. The objective of this study was to develop a deep learning model to diagnose hip dysplasia from plain radiographs and classify dysplastic hips based on their severity.

Authors

  • Martin Magnéli
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA; Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Stockholm, Sweden.
  • Alireza Borjali
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.
  • Eiji Takahashi
    Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA.
  • Michael Axenhus
    Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Stockholm, Sweden. michael.axenhus.2@ki.se.
  • Henrik Malchau
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Sahlgrenska University Hospital, Sweden.
  • Orhun K Moratoglu
    Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA.
  • Kartik M Varadarajan
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, Massachusetts.

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