Deep learning algorithm for fully automated measurement of sagittal balance in adult spinal deformity.

Journal: European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
PMID: 38231388

Abstract

AIM: Deep learning (DL) algorithms can be used for automated analysis of medical imaging. The aim of this study was to assess the accuracy of an innovative, fully automated DL algorithm for analysis of sagittal balance in adult spinal deformity (ASD).

Authors

  • Jannis Löchel
    Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany. jannis.loechel@charite.de.
  • Michael Putzier
    Charité University Hospital, Charitéplatz 1, 10117, Berlin, Germany.
  • Marcel Dreischarf
    RAYLYTIC - medical data automation, Petersstr. 32-34, 04109, Leipzig, Germany.
  • Priyanka Grover
    RAYLYTIC - medical data automation, Petersstr. 32-34, 04109, Leipzig, Germany.
  • Kudaibergen Urinbayev
    RAYLYTIC - medical data automation, Petersstr. 32-34, 04109, Leipzig, Germany.
  • Fahad Abbas
    Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany.
  • Kirsten Labbus
    Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany.
  • Robert Zahn
    Center for Musculoskeletal Surgery, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Augustenburger Platz 1, 13353, Berlin, Germany.

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