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Article: Application of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review

TitleApplication of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review
Authors
KeywordsArtificial intelligence (AI)
Deep learning
Machine learning
Periprosthetic joint infection (PJI)
Replacement
Surgical site infection (SSI)
Total knee arthroplasty (TKA)
Issue Date14-Jun-2023
PublisherSpringer Nature
Citation
Arthroplasty, 2023, v. 5, n. 1 How to Cite?
Abstract

Background

Machine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This systematic review investigated the use of machine learning in preventing periprosthetic joint infection.

Methods

A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed was searched in November 2022. All studies that investigated the clinical applications of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty were included. Non-English studies, studies with no full text available, studies focusing on non-clinical applications of machine learning, reviews and meta-analyses were excluded. For each included study, its characteristics, machine learning applications, algorithms, statistical performances, strengths and limitations were summarized. Limitations of the current machine learning applications and the studies, including their ‘black box’ nature, overfitting, the requirement of a large dataset, the lack of external validation, and their retrospective nature were identified.

Results

Eleven studies were included in the final analysis. Machine learning applications in the prevention of periprosthetic joint infection were divided into four categories: prediction, diagnosis, antibiotic application and prognosis.

Conclusion

Machine learning may be a favorable alternative to manual methods in the prevention of periprosthetic joint infection following total knee arthroplasty. It aids in preoperative health optimization, preoperative surgical planning, the early diagnosis of infection, the early application of suitable antibiotics, and the prediction of clinical outcomes. Future research is warranted to resolve the current limitations and bring machine learning into clinical settings.


Persistent Identifierhttp://hdl.handle.net/10722/337054
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChong, YY-
dc.contributor.authorChan, PK-
dc.contributor.authorChan, VWK-
dc.contributor.authorCheung, A-
dc.contributor.authorLuk, MH-
dc.contributor.authorCheung, MH-
dc.contributor.authorFu, HY-
dc.contributor.authorChiu, KY-
dc.date.accessioned2024-03-11T10:17:44Z-
dc.date.available2024-03-11T10:17:44Z-
dc.date.issued2023-06-14-
dc.identifier.citationArthroplasty, 2023, v. 5, n. 1-
dc.identifier.urihttp://hdl.handle.net/10722/337054-
dc.description.abstract<h3>Background</h3><p>Machine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This systematic review investigated the use of machine learning in preventing periprosthetic joint infection.</p><h3>Methods</h3><p>A systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. PubMed was searched in November 2022. All studies that investigated the clinical applications of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty were included. Non-English studies, studies with no full text available, studies focusing on non-clinical applications of machine learning, reviews and meta-analyses were excluded. For each included study, its characteristics, machine learning applications, algorithms, statistical performances, strengths and limitations were summarized. Limitations of the current machine learning applications and the studies, including their ‘black box’ nature, overfitting, the requirement of a large dataset, the lack of external validation, and their retrospective nature were identified.</p><h3>Results</h3><p>Eleven studies were included in the final analysis. Machine learning applications in the prevention of periprosthetic joint infection were divided into four categories: prediction, diagnosis, antibiotic application and prognosis.</p><h3>Conclusion</h3><p>Machine learning may be a favorable alternative to manual methods in the prevention of periprosthetic joint infection following total knee arthroplasty. It aids in preoperative health optimization, preoperative surgical planning, the early diagnosis of infection, the early application of suitable antibiotics, and the prediction of clinical outcomes. Future research is warranted to resolve the current limitations and bring machine learning into clinical settings.</p>-
dc.languageeng-
dc.publisherSpringer Nature-
dc.relation.ispartofArthroplasty-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial intelligence (AI)-
dc.subjectDeep learning-
dc.subjectMachine learning-
dc.subjectPeriprosthetic joint infection (PJI)-
dc.subjectReplacement-
dc.subjectSurgical site infection (SSI)-
dc.subjectTotal knee arthroplasty (TKA)-
dc.titleApplication of machine learning in the prevention of periprosthetic joint infection following total knee arthroplasty: a systematic review-
dc.typeArticle-
dc.identifier.doi10.1186/s42836-023-00195-2-
dc.identifier.scopuseid_2-s2.0-85161910331-
dc.identifier.volume5-
dc.identifier.issue1-
dc.identifier.eissn2524-7948-
dc.identifier.isiWOS:001006112700001-
dc.identifier.issnl2524-7948-

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