Machine learning in knee arthroplasty: specific data are key-a systematic review.
Artificial intelligence
Knee arthroscopy
Knee surgery
Machine learning
Supervised learning
Total knee arthroplasty
Journal
Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA
ISSN: 1433-7347
Titre abrégé: Knee Surg Sports Traumatol Arthrosc
Pays: Germany
ID NLM: 9314730
Informations de publication
Date de publication:
Feb 2022
Feb 2022
Historique:
received:
16
08
2021
accepted:
16
12
2021
pubmed:
11
1
2022
medline:
26
2
2022
entrez:
10
1
2022
Statut:
ppublish
Résumé
Artificial intelligence (AI) in healthcare is rapidly growing and offers novel options of data analysis. Machine learning (ML) represents a distinct application of AI, which is capable of generating predictions and has already been tested in different medical specialties with various approaches such as diagnostic applications, cost predictions or identification of risk factors. In orthopaedics, this technology has only recently been introduced and the literature on ML in knee arthroplasty is scarce. In this review, we aim to investigate which predictions are already feasible using ML models in knee arthroplasty to identify prerequisites for the effective use of this novel approach. For this reason, we conducted a systematic review of ML algorithms for outcome prediction in knee arthroplasty. A comprehensive search of PubMed, Medline database and the Cochrane Library was conducted to find ML applications for knee arthroplasty. All relevant articles were systematically retrieved and evaluated by an orthopaedic surgeon and a data scientist on the basis of the PRISMA statement. The search strategy yielded 225 articles of which 19 were finally assessed as eligible. A modified Coleman Methodology Score (mCMS) was applied to account for a methodological evaluation. The studies presented in this review demonstrated fair to good results (AUC median 0.76/range 0.57-0.98), while heterogeneous prediction models were analysed: complications (6), costs (4), functional outcome (3), revision (2), postoperative satisfaction (2), surgical technique (1) and biomechanical properties (1) were investigated. The median mCMS was 65 (range 40-80) points. The prediction of distinct outcomes with ML models applying specific data is already feasible; however, the prediction of more complex outcomes is still inaccurate. Registry data on knee arthroplasty have not been fully analysed yet so that specific parameters have not been sufficiently evaluated. The inclusion of specific input data as well as the collaboration of orthopaedic surgeons and data scientists are essential prerequisites to fully utilize the capacity of ML in knee arthroplasty. Future studies should investigate prospective data with specific and longitudinally recorded parameters. III.
Identifiants
pubmed: 35006281
doi: 10.1007/s00167-021-06848-6
pii: 10.1007/s00167-021-06848-6
pmc: PMC8866371
doi:
Types de publication
Journal Article
Review
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
376-388Informations de copyright
© 2022. The Author(s).
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