Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Hinterwimmer:2022:10.1007/s00167-021-06848-6,
author = {Hinterwimmer, F and Lazic, I and Suren, C and Hirschmann, MT and Pohlig, F and Rueckert, D and Burgkart, R and von, Eisenhart-Rothe R},
doi = {10.1007/s00167-021-06848-6},
journal = {Knee Surg Sports Traumatol Arthrosc},
pages = {376--388},
title = {Machine learning in knee arthroplasty: specific data are key-a systematic review.},
url = {http://dx.doi.org/10.1007/s00167-021-06848-6},
volume = {30},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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
AU  - Hinterwimmer,F
AU  - Lazic,I
AU  - Suren,C
AU  - Hirschmann,MT
AU  - Pohlig,F
AU  - Rueckert,D
AU  - Burgkart,R
AU  - von,Eisenhart-Rothe R
DO  - 10.1007/s00167-021-06848-6
EP  - 388
PY  - 2022///
SP  - 376
TI  - Machine learning in knee arthroplasty: specific data are key-a systematic review.
T2  - Knee Surg Sports Traumatol Arthrosc
UR  - http://dx.doi.org/10.1007/s00167-021-06848-6
UR  - https://www.ncbi.nlm.nih.gov/pubmed/35006281
VL  - 30
ER  -
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