• ABSTRACT
    • The rising number of total knee arthroplasty (TKA) revisions combined with the inferior outcomes compared to the primary TKA highlight the critical need for early detection of primary TKA failure. The present work aims to propose a radiomics-based machine learning model to automatically detect TKA failure from radiographs. The dataset comprised radiographs from 44 failed and 51 non-failed TKA patients. Following preprocessing phases, 465 radiomic features were extracted. A cross-validation procedure, consisting in 100 repeated training-validation splits was implemented. The training phase encompassed feature selection, data balancing and machine learning classifier training. Four feature selection approaches were evaluated combined with several classifiers. Based on the average performance metrics on the validation set, the Least Absolute Shrinkage and Selector Operator (LASSO) feature selection, combined with Logistic Regression (LR) classifier achieved the best performance, with an F1-score of 0.701, a balanced accuracy of 0.710 and area under the curve (AUC) of 0.783. The results demonstrate the potentialities of the developed radiomics-based approach in automatically detecting TKA failure from plain radiographs.Clinical Relevance-The increasing number of revision procedures poses significant challenges for healthcare systems, highlighting the critical need for automated early detection of primary TKA failure. The developed model can support clinicians by reducing their workload and minimizing inter- and intra-observer variability.