Comparison of state-of-the-art machine and deep learning algorithms to classify proximal humeral fractures using radiology text.

Journal: European journal of radiology
PMID: 35623313

Abstract

INTRODUCTION: Proximal humeral fractures account for a significant proportion of all fractures. Detailed accurate classification of the type and severity of the fracture is a key component of clinical decision making, treatment and plays an important role in orthopaedic trauma research. This research aimed to assess the performance of Machine Learning (ML) multiclass classification algorithms to classify proximal humeral fractures using radiology text data.

Authors

  • Joanna F Dipnall
    IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.
  • Jueqing Lu
    Department of Data Science & AI, Faculty of Information Technology, Monash University, Australia.
  • Belinda J Gabbe
    School of Public Health and Preventive Medicine, Monash University, Australia; Health Data Research UK, Swansea University Medical School, Swansea University, Singleton Park, Swansea, UK.
  • Filip Cosic
    School of Public Health and Preventive Medicine, Monash University, Australia; Department of Orthopaedic Surgery, The Alfred, Australia.
  • Elton Edwards
    School of Public Health and Preventive Medicine, Monash University, Australia; Department of Orthopaedic Surgery, The Alfred, Australia.
  • Richard Page
    Barwon Centre for Orthopaedic Research and Education (B-CORE), IMPACT, School of Medicine, Deakin University and St John of God Hospital, Australia; Department of Orthopaedics, University Hospital, Barwon Health, Geelong, Victoria, Australia.
  • Lan Du
    Department of Data Science & AI, Faculty of Information Technology, Monash University, Australia.

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