Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach.

Journal: European journal of radiology
Published Date: Oct 23, 2020

Abstract

PURPOSE: Suspected fractures are among the most common reasons for patients to visit emergency departments and often can be difficult to detect and analyze them on film scans. Therefore, we aimed to design a Deep Learning-based tool able to help doctors in diagnosis of bone fractures, following the hierarchical classification proposed by the Arbeitsgemeinschaft für Osteosynthesefragen (AO) Foundation and the Orthopaedic Trauma Association (OTA).

Authors

  • Leonardo Tanzi
    DIGEP, Polytechnic University of Turin, Torino, Italy.
  • Enrico Vezzetti
    Department of Management and Production Engineer, Politechnic University of Turin, Turin, Italy.
  • Rodrigo Moreno
    Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden.
  • Alessandro Aprato
    School of Medicine, University of Turin, Viale 25 Aprile 137 int 6, 10133, Torino, Italy. Electronic address: ale_aprato@hotmail.com.
  • Andrea Audisio
    School of Medicine, University of Turin, Viale 25 Aprile 137 int 6, 10133, Torino, Italy. Electronic address: andrea.audisio384@edu.unito.it.
  • Alessandro Massè
    School of Medicine, University of Turin, Viale 25 Aprile 137 int 6, 10133, Torino, Italy. Electronic address: alessandro.masse@unito.it.

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