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Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol

BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do s...

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Autores principales: Maffulli, Nicola, Rodriguez, Hugo C., Stone, Ian W., Nam, Andrew, Song, Albert, Gupta, Manu, Alvarado, Rebecca, Ramon, David, Gupta, Ashim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570027/
https://www.ncbi.nlm.nih.gov/pubmed/33076945
http://dx.doi.org/10.1186/s13018-020-02002-z
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author Maffulli, Nicola
Rodriguez, Hugo C.
Stone, Ian W.
Nam, Andrew
Song, Albert
Gupta, Manu
Alvarado, Rebecca
Ramon, David
Gupta, Ashim
author_facet Maffulli, Nicola
Rodriguez, Hugo C.
Stone, Ian W.
Nam, Andrew
Song, Albert
Gupta, Manu
Alvarado, Rebecca
Ramon, David
Gupta, Ashim
author_sort Maffulli, Nicola
collection PubMed
description BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery. METHODS: A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish, and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions and acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies and do not relate to orthopedic interventions or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and the results of meta-analyses will not be included in the analysis. DISCUSSION: Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed. TRIAL REGISTRATION: The protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement.
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spelling pubmed-75700272020-10-20 Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol Maffulli, Nicola Rodriguez, Hugo C. Stone, Ian W. Nam, Andrew Song, Albert Gupta, Manu Alvarado, Rebecca Ramon, David Gupta, Ashim J Orthop Surg Res Study Protocol BACKGROUND: Artificial intelligence (AI) and machine learning (ML) are interwoven into our everyday lives and have grown enormously in some major fields in medicine including cardiology and radiology. While these specialties have quickly embraced AI and ML, orthopedic surgery has been slower to do so. Fortunately, there has been a recent surge in new research emphasizing the need for a systematic review. The primary objective of this systematic review will be to provide an update on the advances of AI and ML in the field of orthopedic surgery. The secondary objectives will be to evaluate the applications of AI and ML in providing a clinical diagnosis and predicting post-operative outcomes and complications in orthopedic surgery. METHODS: A systematic search will be conducted in PubMed, ScienceDirect, and Google Scholar databases for articles written in English, Italian, French, Spanish, and Portuguese language articles published up to September 2020. References will be screened and assessed for eligibility by at least two independent reviewers as per PRISMA guidelines. Studies must apply to orthopedic interventions and acute and chronic orthopedic musculoskeletal injuries to be considered eligible. Studies will be excluded if they are animal studies and do not relate to orthopedic interventions or if no clinical data were produced. Gold standard processes and practices to obtain a clinical diagnosis and predict post-operative outcomes shall be compared with and without the use of ML algorithms. Any case reports and other primary studies assessing the prediction rate of post-operative outcomes or the ability to identify a diagnosis in orthopedic surgery will be included. Systematic reviews or literature reviews will be examined to identify further studies for inclusion, and the results of meta-analyses will not be included in the analysis. DISCUSSION: Our findings will evaluate the advances of AI and ML in the field of orthopedic surgery. We expect to find a large quantity of uncontrolled studies and a smaller subset of articles describing actual applications and outcomes for clinical care. Cohort studies and large randomized control trial will likely be needed. TRIAL REGISTRATION: The protocol will be registered on PROSPERO international prospective register of systematic reviews prior to commencement. BioMed Central 2020-10-19 /pmc/articles/PMC7570027/ /pubmed/33076945 http://dx.doi.org/10.1186/s13018-020-02002-z Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Study Protocol
Maffulli, Nicola
Rodriguez, Hugo C.
Stone, Ian W.
Nam, Andrew
Song, Albert
Gupta, Manu
Alvarado, Rebecca
Ramon, David
Gupta, Ashim
Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
title Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
title_full Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
title_fullStr Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
title_full_unstemmed Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
title_short Artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
title_sort artificial intelligence and machine learning in orthopedic surgery: a systematic review protocol
topic Study Protocol
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7570027/
https://www.ncbi.nlm.nih.gov/pubmed/33076945
http://dx.doi.org/10.1186/s13018-020-02002-z
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