Detection of Prosthetic Loosening in Hip and Knee Arthroplasty Using Machine Learning: A Systematic Review and Meta-Analysis

Abstract

Background: prosthetic loosening after hip and knee arthroplasty is one of the most common causes of joint arthroplasty failure and revision surgery. Diagnosis of prosthetic loosening is a difficult problem and, in many cases, loosening is not clearly diagnosed until accurately confirmed during surgery. The purpose of this study is to conduct a systematic review and meta-analysis to demonstrate the analysis and performance of machine learning in diagnosing prosthetic loosening after total hip arthroplasty (THA) and total knee arthroplasty (TKA). Materials and Methods: three comprehensive databases, including MEDLINE, EMBASE, and the Cochrane Library, were searched for studies that evaluated the detection accuracy of loosening around arthroplasty implants using machine learning. Data extraction, risk of bias assessment, and meta-analysis were performed. Results: five studies were included in the meta-analysis. All studies were retrospective studies. In total, data from 2013 patients with 3236 images were assessed; these data involved 2442 cases (75.5%) with THAs and 794 cases (24.5%) with TKAs. The most common and best-performing machine learning algorithm was DenseNet. In one study, a novel stacking approach using a random forest showed similar performance to DenseNet. The pooled sensitivity across studies was 0.92 (95% CI 0.84-0.97), the pooled specificity was 0.95 (95% CI 0.93-0.96), and the pooled diagnostic odds ratio was 194.09 (95% CI 61.60-611.57). The I2 statistics for sensitivity and specificity were 96% and 62%, respectively, showing that there was significant heterogeneity. The summary receiver operating characteristics curve indicated the sensitivity and specificity, as did the prediction regions, with an AUC of 0.9853. Conclusions: the performance of machine learning using plain radiography showed promising results with good accuracy, sensitivity, and specificity in the detection of loosening around THAs and TKAs. Machine learning can be incorporated into prosthetic loosening screening programs.

Keywords: arthroplasty; loosening; machine learning; prosthesis; review; transfer learning.

Figure 1

PRISMA flow diagram for systematic review.

Figure 2

Forest plots for sensitivity (a) and specificity (b). GLMM: generalized linear mixed model, CI: confidence interval, THA: total hip arthroplasty, TKA: total knee arthroplasty [32,33,34,35,36].

Figure 3

Forest plots diagnostic odds ratio. IV: interval variance; CI: confidence interval, THA: total hip arthroplasty; TKA: total knee arthroplasty; DOR: diagnostic odds ratio [32,33,34,35,36].

Figure 4

Summary receiver operating characteristics (sROC) curve, the calculated area under the curve (AUC) = 0.985. SROC: summary receiver operating characteristics; SE: standard error; AUC: Area under the curve.

Figure 5

Methodological assessment by QUADAS-2 [32,33,34,35,36].

Figure 5

Methodological assessment by QUADAS-2 [32,33,34,35,36].

Figure 6

Funnel plot.