Automated diagnosis and classification of metacarpal and phalangeal fractures using a convolutional neural network: a retrospective data analysis study.

Journal: Acta orthopaedica
PMID: 39786203

Abstract

BACKGROUND AND PURPOSE:  Hand fractures are commonly presented in emergency departments, yet diagnostic errors persist, leading to potential complications. The use of artificial intelligence (AI) in fracture detection has shown promise, but research focusing on hand metacarpal and phalangeal fractures remains limited. We aimed to train and evaluate a convolutional neural network (CNN) model to diagnose metacarpal and phalangeal fractures using plain radiographs according to the AO/OTA classification system and custom classifiers.

Authors

  • Michael Axenhus
    Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Stockholm, Sweden. michael.axenhus.2@ki.se.
  • Anna Wallin
    Department of Clinical Sciences at Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
  • Jonas Havela
    Department of Clinical Sciences at Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
  • Sara Severin
    Department of Orthopaedic Surgery, Danderyd Hospital, Stockholm; Department of Clinical Sciences at Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
  • Ablikim Karahan
    Department of Clinical Sciences at Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
  • Max Gordon
    a Department of Clinical Sciences , Karolinska Institutet , Danderyd Hospital.
  • Martin Magnéli
    Department of Orthopaedic Surgery, Harris Orthopaedics Laboratory, Massachusetts General Hospital, Boston, MA, USA; Department of Orthopaedic Surgery, Harvard Medical School, Boston, MA, USA; Karolinska Institutet, Department of Clinical Sciences, Danderyd Hospital, Stockholm, Sweden.

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