Fully automated deep learning for knee alignment assessment in lower extremity radiographs: a cross-sectional diagnostic study.

Journal: Skeletal radiology
Published Date: Nov 13, 2021

Abstract

OBJECTIVES: Accurate assessment of knee alignment and leg length discrepancy is currently measured manually from standing long-leg radiographs (LLR), a process that is both time consuming and poorly reproducible. The aim was to assess the performance of a commercial available AI software by comparing its outputs with manually performed measurements.

Authors

  • Sebastian Simon
    Michael-Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna-Speising, Speisinger Straße 109, 1130, Vienna, Austria.
  • Gilbert M Schwarz
    Michael-Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna-Speising, Speisinger Straße 109, 1130, Vienna, Austria.
  • Alexander Aichmair
    Michael-Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna-Speising, Speisinger Straße 109, 1130, Vienna, Austria.
  • Bernhard J H Frank
    Michael-Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna-Speising, Speisinger Straße 109, 1130, Vienna, Austria.
  • Allan Hummer
    Image Biopsy Lab GmbH, Zehetnergasse 6/2/2, 1140, Vienna, Austria.
  • Matthew D DiFranco
    Image Biopsy Lab GmbH, Zehetnergasse 6/2/2, 1140, Vienna, Austria.
  • Martin Dominkus
    II. Department of Orthopaedic Surgery, Orthopaedic Hospital Vienna-Speising, Speisinger Straße 109, 1130, Vienna, Austria.
  • Jochen G Hofstaetter
    Michael-Ogon Laboratory for Orthopaedic Research, Orthopaedic Hospital Vienna-Speising, Speisinger Straße 109, 1130, Vienna, Austria. researchlab@oss.at.

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