AI-based X-ray fracture analysis of the distal radius: accuracy between representative classification, detection and segmentation deep learning models for clinical practice.

Journal: BMJ open
PMID: 38262641

Abstract

OBJECTIVES: To aid in selecting the optimal artificial intelligence (AI) solution for clinical application, we directly compared performances of selected representative custom-trained or commercial classification, detection and segmentation models for fracture detection on musculoskeletal radiographs of the distal radius by aligning their outputs.

Authors

  • Maximilian Frederik Russe
    Department of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany.
  • Philipp Rebmann
    Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center-University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany.
  • Phuong Hien Tran
    Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Elias Kellner
    Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Marco Reisert
    Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
  • Fabian Bamberg
    Department of Diagnostic and Interventional Radiology, University Medical Center Tübingen, Tübingen, Germany.
  • Elmar Kotter
    Department of Diagnostic and Interventional Radiology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany.
  • Suam Kim
    Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Medical Center-University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany. suam.kim@uniklinik-freiburg.de.

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