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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.

Purpose The number of primary total knee arthroplasties (TKA) is expected to rise constantly. For patients and healthcare providers, the early identification of risk factors therefore becomes increasingly fundamental in the context of precision medicine. Others have already investigated the detection of risk factors by conducting literature reviews and applying conventional statistical methods. Since the prediction of events has been moderately accurate, a more comprehensive approach is needed. Machine learning (ML) algorithms have had ample success in many disciplines. However, these methods have not yet had a significant impact in orthopaedic research. The selection of a data source as well as the inclusion of relevant parameters is of utmost importance in this context. In this study, a standardized approach for ML in TKA to predict complications during surgery and an irregular surgery duration using data from two German arthroplasty-specific registries was evaluated. Methods The dataset is based on two initiatives of the German Society for Orthopaedics and Orthopaedic Surgery. A problem statement and initial parameters were defined. After screening, cleaning and preparation of these datasets, 864 cases of primary TKA (2016–2019) were gathered. The XGBoost algorithm was chosen and applied with a hyperparameter search, a cross validation and a loss weighting to cope with class imbalance. For final evaluation, several metrics (accuracy, sensitivity, specificity, AUC) were calculated. Results An accuracy of 92.0%, sensitivity of 34.8%, specificity of 95.8%, and AUC of 78.0% were achieved for predicting complications in primary TKA and 93.4%, 74.0%, 96.3%, and 91.6% for predicting irregular surgery duration, respectively. While traditional statistics (correlation coefficient) could not find any relevant correlation between any two parameters, the feature importance revealed several non-linear outcomes. Conclusion In this study, a feasible ML model to predict outcomes of primary TKA with very promising results was built. Complex correlations between parameters were detected, which could not be recognized by conventional statistical analysis. Arthroplasty-specific data were identified as relevant by the ML model and should be included in future clinical applications. Furthermore, an interdisciplinary interpretation as well as evaluation of the results by a data scientist and an orthopaedic surgeon are of paramount importance. Level of evidence Level IV.

Despite increasing numbers of primary hip arthroplasties performed through the direct anterior approach (DAA), there is a lack of literature on DAA revision arthroplasty. The present study was performed in order to evaluate outcomes and revision rates after revision through the DAA using an asymmetric acetabular component with optional intra- and extramedullary fixation. In a retrospective cohort study, we analyzed prospectively collected data of 57 patients (61 hips, 43 female, 18 male) who underwent aseptic acetabular component revision through the DAA with the abovementioned implant system between January 2015 and December 2017. The mean follow-up was 40 months (12–56). Survival rates were estimated using the Kaplan–Meier method. All complications were documented and functional outcomes were assessed pre- and postoperatively. Kaplan–Meier analysis revealed an estimated five-year implant survival of 97% (confidence interval CI 87–99%). The estimated five-year survival with revision for any cause was 93% (CI 83–98%). The overall revision rate was 6.6% (n = 4). Two patients had to undergo revision due to periprosthetic infection (3.3%). In one patient, the acetabular component was revised due to aseptic loosening four months postoperatively. Another patient suffered from postoperative iliopsoas impingement and was treated successfully by arthroscopic iliopsoas tenotomy. Two (3.3%) of the revised hips dislocated postoperatively. The mean Harris Hip Score improved from 35 (2–66) preoperatively to 86 (38–100) postoperatively (p < 0.001). The hip joint’s anatomical center of rotation was restored at a high degree of accuracy. Our findings demonstrate that acetabular revision arthroplasty through the DAA using an asymmetric acetabular component with optional intra- and extramedullary fixation is safe and practicable, resulting in good radiographic and clinical midterm results.

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Background: Unicompartmental knee arthroplasty is an established treatment option for anteromedial osteoarthritis. However, large registry studies report higher rates of aseptic loosening compared to total knee arthroplasty. The objective of this study was to assess the impact of bone density on morphological cement penetration. Moreover, an alternative regional bone density measuring technique was validated against the established bone mineral density assessment. Methods: Components were implanted on the medial side of 18 fresh-frozen cadaver knees using a minimally invasive approach. Bone density has been quantified prior to implantation using Hounsfield units and bone mineral density. Morphological cement penetration has been assessed in different areas and was correlated with local bone density. Findings: A highly significant correlation between Hounsfield units and trabecular bone mineral density was detected (r = 0.93; P < 0.0001), and local bone density was significantly increased in the anterior and posterior area (P = 0.0003). The mean cement penetration depth was 1.5 (SD 0.5 mm), and cement intrusion into trabecular bone was interrupted in 31.8% (SD 23.7%) of the bone-cement interface. Bone density was correlated significantly negative with penetration depth (r = − 0.31; P = 0.023) and positive with interruptions of horizontal interdigitating (r = + 0.33; P = 0.014). Cement penetration around the anchoring peg was not significantly correlated with bone density. Interpretation: Areas with high bone density were characterized by significantly lower penetration depths and significantly higher areas without cement penetration. Anchoring pegs facilitate cement intrusion mechanically. Regional quantification of bone density using Hounsfield units is a simple but valuable extension to the established determination of bone mineral density.

The diagnosis and treatment of periprosthetic joint infection (PJI) currently relies on cultures, which are time-consuming and often fail. Multiplex PCR assays promise reliable and prompt results, but have been heterogeneously evaluated. In this study, we analyse multiplex PCR in pathogen identification using only tissue biopsies. 42 patients after revision arthroplasty of the hip or knee were evaluated using multiplex PCR to identify microorganisms. The patients were classified according to the diagnostic criteria published by Zimmerli et al. and the results were compared to the respective microbiological cultures. PJI was detected in 15 patients and 27 revisions were aseptic. The multiplex PCR of tissue biopsies had a sensitivity of 0.3 (95% CI 0.12–0.62), a specificity of 1.0 (0.87–1.0), a positive predictive value of 1.0 (0.48–1.0) and a negative predictive value of 0.73 (0.56–0.86). The diagnostic accuracy of multiplex PCR on tissue biopsy samples is low in comparison to routine microbiological cultures. The evaluation of tissue biopsies using multiplex PCR was prone to false negative results. However, multiplex PCR assays have the advantage of rapid pathogen identification. We therefore recommend further investigation of multiplex PCR in the setting of suspected PJI with a careful choice of specimens.