Using statistical modelling and machine learning in detecting bone properties: A systematic review protocol.

Journal: PloS one
PMID: 40067789

Abstract

INTRODUCTION: Osteoporosis, a common condition characterised by decreased bone mass and microarchitectural deterioration, leading to increased fracture risk, is a significant health concern. Traditional diagnostic methods, such as Dual-energy X-ray Absorptiometry (DXA), have limitations in sensitivity and accessibility. However, the emergence of artificial intelligence (AI) and machine learning (ML) has brought promising tools capable of analysing complex medical data to enhance the detection and prediction of osteoporosis-related bone properties. This systematic review protocol outlines the methodology to evaluate the application and effectiveness of AI and ML methods in detecting bone properties and osteoporosis. It underscores their potential to revolutionise healthcare by providing more accurate and accessible osteoporosis detection and prediction tools.

Authors

  • Osama Abdelhay
    Department of Data Science and Artificial Intelligence, Princess Sumaya University for Technology, Amman, Jordan.
  • Rand Alshoubaki
    Department of Data Science and Artificial Intelligence, Princess Sumaya University for Technology, Amman, Jordan.
  • Sana Murad
    Department of Data Science and Artificial Intelligence, Princess Sumaya University for Technology, Amman, Jordan.
  • Omar Abdel-Hafez
    Department of Data Science and Artificial Intelligence, Princess Sumaya University for Technology, Amman, Jordan.
  • Qusai Abdelhay
    Department of Orthopaedic Surgery, Al-Bashir Hospital, Amman, Jordan.
  • Bassem Haddad
    Division of Orthopaedic, Department of Special Surgery, School of Medicine, The University of Jordan, Amman, Jordan.
  • Tasneem Alhosanie
    Division of Orthopaedic, Department of Special Surgery, School of Medicine, The University of Jordan, Amman, Jordan.
  • Hala Ajlouni
    Division of Orthopaedic, Department of Special Surgery, School of Medicine, The University of Jordan, Amman, Jordan.
  • Leanne Ajlouni
    Division of Orthopaedic, Department of Special Surgery, School of Medicine, The University of Jordan, Amman, Jordan.
  • Tareq Qarain
    Division of Orthopaedic, Department of Special Surgery, School of Medicine, The University of Jordan, Amman, Jordan.
  • Hamzeh Murad
    Division of Orthopaedic, Department of Special Surgery, School of Medicine, The University of Jordan, Amman, Jordan.
  • Taghreed Altamimi
    Software Engineering Department, Alfaisal University, Riyadh, Saudi Arabia.

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