Knee Arthrodesis

Kerr, Glenn J.; Parvızı, Javad

doi:10.1007/978-1-4614-7928-4_17

Cited by 2 publications

(1 citation statement)

References 62 publications

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“…Treatment options for PJI include DAIR, one- or two-stage rTKA, arthrodesis and amputation [ 29 ]. Among them, DAIR and rTKA are the two most common choices, but the success rates of these treatments vary greatly.…”

Section: Discussionmentioning

confidence: 99%

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

Chong

Chan

et al. 2023

Arthroplasty

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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.

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Section: Discussionmentioning

confidence: 99%