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

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 uti...

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Main Authors:	Alzubaidi L., AL-Dulaimi K., Salhi A., Alammar Z., Fadhel M.A., Albahri A.S., Alamoodi A.H., Albahri O.S., Hasan A.F., Bai J., Gilliland L., Peng J., Branni M., Shuker T., Cutbush K., Santamar�a J., Moreira C., Ouyang C., Duan Y., Manoufali M., Jomaa M., Gupta A., Abbosh A., Gu Y.
Other Authors:	57195380379
Format:	Review
Published:	Elsevier B.V. 2025
Subjects:	Deep Learning Humans Orthopedics Deep learning Diagnosis Fracture Bone tumor Fracture detection Knowledge gaps Orthopaedic applications Orthopaedic surgeons Orthopedic technology Osteoarthritis Trustworthy AI Tumor diagnosis arthroplasty bone age determination bone implant bone tumor deep learning diagnosis fairness Food and Drug Administration fracture human knowledge gap MRI scanner nuclear magnetic resonance imaging orthopedic surgeon orthopedics osteoarthritis prediction review soft tissue disease therapy treatment planning trustworthiness tumor diagnosis Web of Science procedures
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spelling	my.uniten.dspace-364122025-03-03T15:42:18Z Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion Alzubaidi L. AL-Dulaimi K. Salhi A. Alammar Z. Fadhel M.A. Albahri A.S. Alamoodi A.H. Albahri O.S. Hasan A.F. Bai J. Gilliland L. Peng J. Branni M. Shuker T. Cutbush K. Santamar�a J. Moreira C. Ouyang C. Duan Y. Manoufali M. Jomaa M. Gupta A. Abbosh A. Gu Y. 57195380379 57193131833 57196190467 58112049000 57192639808 57201009814 57205435311 57201013684 59237084100 57217198195 57814269800 59236953800 58925397700 57216973352 23992433600 56211885400 51663717600 14008574600 7202190080 54894519300 59061554100 57198676774 13404674100 7403046386 Deep Learning Humans Orthopedics Deep learning Diagnosis Fracture Bone tumor Deep learning Fracture detection Knowledge gaps Orthopaedic applications Orthopaedic surgeons Orthopedic technology Osteoarthritis Trustworthy AI Tumor diagnosis arthroplasty bone age determination bone implant bone tumor deep learning diagnosis fairness Food and Drug Administration fracture human knowledge gap MRI scanner nuclear magnetic resonance imaging orthopedic surgeon orthopedics osteoarthritis prediction review soft tissue disease therapy treatment planning trustworthiness tumor diagnosis Web of Science procedures Orthopedics 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. ? 2024 The Author(s) Final 2025-03-03T07:42:18Z 2025-03-03T07:42:18Z 2024 Review 10.1016/j.artmed.2024.102935 2-s2.0-85199786274 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199786274&doi=10.1016%2fj.artmed.2024.102935&partnerID=40&md5=f27e678ec6e059d7834e8675e7e1984a https://irepository.uniten.edu.my/handle/123456789/36412 155 102935 All Open Access; Hybrid Gold Open Access Elsevier B.V. Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Deep Learning Humans Orthopedics Deep learning Diagnosis Fracture Bone tumor Deep learning Fracture detection Knowledge gaps Orthopaedic applications Orthopaedic surgeons Orthopedic technology Osteoarthritis Trustworthy AI Tumor diagnosis arthroplasty bone age determination bone implant bone tumor deep learning diagnosis fairness Food and Drug Administration fracture human knowledge gap MRI scanner nuclear magnetic resonance imaging orthopedic surgeon orthopedics osteoarthritis prediction review soft tissue disease therapy treatment planning trustworthiness tumor diagnosis Web of Science procedures Orthopedics
spellingShingle	Deep Learning Humans Orthopedics Deep learning Diagnosis Fracture Bone tumor Deep learning Fracture detection Knowledge gaps Orthopaedic applications Orthopaedic surgeons Orthopedic technology Osteoarthritis Trustworthy AI Tumor diagnosis arthroplasty bone age determination bone implant bone tumor deep learning diagnosis fairness Food and Drug Administration fracture human knowledge gap MRI scanner nuclear magnetic resonance imaging orthopedic surgeon orthopedics osteoarthritis prediction review soft tissue disease therapy treatment planning trustworthiness tumor diagnosis Web of Science procedures Orthopedics Alzubaidi L. AL-Dulaimi K. Salhi A. Alammar Z. Fadhel M.A. Albahri A.S. Alamoodi A.H. Albahri O.S. Hasan A.F. Bai J. Gilliland L. Peng J. Branni M. Shuker T. Cutbush K. Santamar�a J. Moreira C. Ouyang C. Duan Y. Manoufali M. Jomaa M. Gupta A. Abbosh A. Gu Y. Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion
description	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. ? 2024 The Author(s)
author2	57195380379
author_facet	57195380379 Alzubaidi L. AL-Dulaimi K. Salhi A. Alammar Z. Fadhel M.A. Albahri A.S. Alamoodi A.H. Albahri O.S. Hasan A.F. Bai J. Gilliland L. Peng J. Branni M. Shuker T. Cutbush K. Santamar�a J. Moreira C. Ouyang C. Duan Y. Manoufali M. Jomaa M. Gupta A. Abbosh A. Gu Y.
format	Review
author	Alzubaidi L. AL-Dulaimi K. Salhi A. Alammar Z. Fadhel M.A. Albahri A.S. Alamoodi A.H. Albahri O.S. Hasan A.F. Bai J. Gilliland L. Peng J. Branni M. Shuker T. Cutbush K. Santamar�a J. Moreira C. Ouyang C. Duan Y. Manoufali M. Jomaa M. Gupta A. Abbosh A. Gu Y.
author_sort	Alzubaidi L.
title	Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion
title_short	Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion
title_full	Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion
title_fullStr	Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion
title_full_unstemmed	Comprehensive review of deep learning in orthopaedics: Applications, challenges, trustworthiness, and fusion
title_sort	comprehensive review of deep learning in orthopaedics: applications, challenges, trustworthiness, and fusion
publisher	Elsevier B.V.
publishDate	2025
_version_	1825816021373550592
score	13.244413

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

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