Evaluation of a newly designed deep learning-based algorithm for automated assessment of scapholunate distance in wrist radiography as a surrogate parameter for scapholunate ligament rupture and the correlation with arthroscopy.

Journal: La Radiologia medica
PMID: 37726593

Abstract

PURPOSE: Not diagnosed or mistreated scapholunate ligament (SL) tears represent a frequent cause of degenerative wrist arthritis. A newly developed deep learning (DL)-based automated assessment of the SL distance on radiographs may support clinicians in initial image interpretation.

Authors

  • Gabriel Keller
    Department of Diagnostic and Interventional Radiology, Eberhard Karls University Tuebingen, Hoppe-Seyler-Strasse 3, 72076, Tuebingen, Germany.
  • Katarzyna Rachunek
    Department of Hand, Plastic, Reconstructive and Burn Surgery, BG Trauma Center Tübingen, Eberhard Karls University of Tübingen, 72076, Tübingen, Germany.
  • Fabian Springer
    Department of Diagnostic & Interventional Radiology, University Hospital Tübingen, Tübingen, Germany.
  • Mathias Kraus
    Institute of Information Systems, FAU Erlangen-Nuremberg, Nuremberg, Germany.

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