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.

The role of bone mineral density and cartilage volume to predict knee cartilage degeneration

“…This particular case is confirmed even by several recent reviews which proved evidence these types of features were not found in previous screenings. 23,24 Consequently, we will briefly discuss, in the following of this section, papers in the fields with a similar objective, still focusing on the predictive power of features for effective/potential classification studies. For instance, in 2020 Jafarzadeh and co-workers presented a preliminary study whose aim was to assess if both clinical and imaging features of OA could help to predict knee replacement over a 7-year period, including knees with and without radiographic OA.…”

“…This particular case is confirmed even by several recent reviews which proved evidence these types of features were not found in previous screenings. 23 , 24 …”

The current role of the virtual elements of artificial intelligence in total knee arthroplasty

“…In a systematic review with grade III of evidence, Hinterwimmer et al analyzed ML algorithms for outcome prediction in TKA ( 17 ). The studies presented in such a review showed fair to good outcomes (AUC median 0.76/range 0.57–0.98), while heterogeneous prediction models were analyzed: complications ( 6 ), costs ( 4 ), functional result ( 3 ), revision ( 2 ), satisfaction after surgery ( 2 ), surgical procedure ( 1 ), and biomechanical properties ( 1 ) were studied.…”

“… • Validation of wearable and ML-based surveillance platforms ( 15 ). • Prediction of distinct results with ML models applying specific data ( 17 ). Deep learning (DL) • Accurate identification of the presence of TKA and differentiation between specific arthroplasty designs ( 18 ).…”

Artificial intelligence and robotics in TKA surgery: promising options for improved outcomes?

“…Machine learning represents a distinct application of AI, which describes algorithms for automatic and incremental function optimization. This can be used to make predictions by detecting non-linear relationships in large data sets [ 5 ]. Both offer tremendous new possibilities and clearly are promising options for the field of orthopaedic surgery, and in particular for total knee arthroplasty (TKA).…”