Machine learning in knee arthroplasty: specific data are key-a systematic review

Abstract

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 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.

Level of evidence: III.

Keywords: Artificial intelligence; Knee arthroscopy; Knee surgery; Machine learning; Supervised learning; Total knee arthroplasty.

Fig. 1

Flow diagram. The initial search through the PubMed and Medline database as well as Cochrane Library resulted in 225 publications (March 2021). After screening the titles and abstracts, 200 were excluded and 25 remained. After applying the exclusion criteria, another 6 were excluded and 19 remained for final investigation

Fig. 2

Overview of the development of a ML model in knee surgery. After defining the problem statement, the algorithm development consists of three main pillars: a data, b algorithm, c results. In step a, the dataset has to be established and prepared in a manner that it is qualitatively and quantitatively feasible for ML algorithms. In step b, an algorithm has to be chosen or developed and fine-tuned for the specific problem at hand. In step c, the results have to be evaluated by a computer scientist in collaboration with an orthopaedic surgeon. If the results are not yet satisfying, steps b and c can be iterated several times