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Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion

Alzubaidi L 1 , Al-Dulaimi K 2 , Salhi A 3 , Alammar Z 4 , Fadhel MA 5 , Albahri AS 6 Show all authors , Alamoodi AH 7 , Albahri OS 8 , Hasan AF 9 , Bai J 10 , Gilliland L 3 , Peng J 5 , Branni M 3 , Shuker T 11 , Cutbush K 11 , Santamaría J 12 , Moreira C 13 , Ouyang C 14 , Duan Y 15 , Manoufali M 16 , Jomaa M 11 , Gupta A 17 , Abbosh A 18 , Gu Y 10

Affiliations 

  • 1 School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia. Electronic address: l.alzubaidi@qut.edu.au
  • 2 Computer Science Department, College of Science, Al-Nahrain University, Baghdad, Baghdad 10011, Iraq; School of Electrical Engineering and Robotics, Queensland University of Technology, Brisbane, QLD 4000, Australia
  • 3 QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
  • 4 School of Computer Science, Queensland University of Technology, Brisbane, QLD 4000, Australia
  • 5 Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
  • 6 Technical College, Imam Ja'afar Al-Sadiq University, Baghdad, Iraq
  • 7 Institute of Informatics and Computing in Energy, Universiti Tenaga Nasional, Kajang 43000, Malaysia
  • 8 Australian Technical and Management College, Melbourne, Australia
  • 9 Faculty of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
  • 10 School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia
  • 11 QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; St Andrew's War Memorial Hospital, Brisbane, QLD 4000, Australia
  • 12 Department of Computer Science, University of Jaén, Jaén 23071, Spain
  • 13 Data Science Institute, University of Technology Sydney, Australia
  • 14 School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia
  • 15 School of Computing, Clemson University, Clemson, 29631, SC, USA
  • 16 CSIRO, Kensington, WA 6151, Australia; School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
  • 17 School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD 4000, Australia; QUASR/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD 4000, Australia; Research and Development department, Akunah Med Technology Pty Ltd Co, Brisbane, QLD 4120, Australia
  • 18 School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4067, Australia
Artif Intell Med, 2024 Sep;155:102935.
PMID: 39079201 DOI: 10.1016/j.artmed.2024.102935

Abstract

Deep learning (DL) in orthopaedics has gained significant attention in recent years. Previous studies have shown that DL can be applied to a wide variety of orthopaedic tasks, including fracture detection, bone tumour diagnosis, implant recognition, and evaluation of osteoarthritis severity. The utilisation of DL is expected to increase, owing to its ability to present accurate diagnoses more efficiently than traditional methods in many scenarios. This reduces the time and cost of diagnosis for patients and orthopaedic surgeons. To our knowledge, no exclusive study has comprehensively reviewed all aspects of DL currently used in orthopaedic practice. This review addresses this knowledge gap using articles from Science Direct, Scopus, IEEE Xplore, and Web of Science between 2017 and 2023. The authors begin with the motivation for using DL in orthopaedics, including its ability to enhance diagnosis and treatment planning. The review then covers various applications of DL in orthopaedics, including fracture detection, detection of supraspinatus tears using MRI, osteoarthritis, prediction of types of arthroplasty implants, bone age assessment, and detection of joint-specific soft tissue disease. We also examine the challenges for implementing DL in orthopaedics, including the scarcity of data to train DL and the lack of interpretability, as well as possible solutions to these common pitfalls. Our work highlights the requirements to achieve trustworthiness in the outcomes generated by DL, including the need for accuracy, explainability, and fairness in the DL models. We pay particular attention to fusion techniques as one of the ways to increase trustworthiness, which have also been used to address the common multimodality in orthopaedics. Finally, we have reviewed the approval requirements set forth by the US Food and Drug Administration to enable the use of DL applications. As such, we aim to have this review function as a guide for researchers to develop a reliable DL application for orthopaedic tasks from scratch for use in the market.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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