An AI-based system for fully automated knee alignment assessment in standard AP knee radiographs.

Journal: The Knee
Published Date: Mar 3, 2025

Abstract

BACKGROUND: Accurate assessment of knee alignment in pre- and post-operative radiographs is crucial for knee arthroplasty planning and evaluation. Current methods rely on manual alignment assessment, which is time-consuming and error-prone. This study proposes a machine learning-based approach to fully automatically measure anatomical varus/valgus alignment in standard anteroposterior (AP) knee radiographs.

Authors

  • Dominic Cullen
    Division of Informatics, Imaging and Data Sciences, School of Health Sciences, The University of Manchester, United Kingdom; Northern Care Alliance NHS Foundation Trust, Salford, United Kingdom.
  • Peter Thompson
    Division of Informatics, Imaging and Data Sciences, School of Health Sciences, The University of Manchester, United Kingdom.
  • David Johnson
    Department of Urology, University of North Carolina at Chapel Hill, Chapel Hill, NC.
  • Claudia Lindner
    Division of Informatics, Imaging & Data Sciences, School of Health Sciences, FBMH, The University of Manchester, M13 9PT, UK.

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