Robotic (RTKA) and computer-navigated total knee arthroplasty (CNTKA) are increasingly replacing manual techniques in orthopaedic surgery. This systematic review compared clinical outcomes associated with RTKA and CNTKA and investigated the utility of natural language processing (NLP) for the literature synthesis.


A comprehensive search strategy was implemented. Results of included studies were combined and analysed. A transfer learning approach was applied to train deep NLP classifiers (BERT, RoBERTa and XLNet), with cross-validation, to partially automate the systematic review process.


52 studies were included, comprising 5,067 RTKA and 2,108 CNTKA. Complication rates were 0–22% and 0–16% and surgical time was 70–116 and 77–102 min for RTKA and CNTKA, respectively. Technical failures were more commonly associated with RTKA (8%) than CNTKA (2–4%). Patient satisfaction was equivalent (94%). RTKA was associated with a higher likelihood of achieving target alignment, less femoral notching, shorter operative time and shorter length of stay. NLP models demonstrated moderate performance (AUC = 0.65–0.68).


RTKA and CNTKA appear to be associated with similarly positive clinical outcomes. Further work is required to determine whether the two techniques differ significantly with regard to specific outcome measures. NLP shows promise for facilitating the systematic review process.


Total knee arthroplasty, Natural language processing, Robotic arthroplasty, Computer-navigated arthroplasty

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