Prosthesis, Volume 6, Issue 4
August 2024 - 20 articles
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Cover Story: Joint registries aim to reduce the revision rates of arthroplasty surgeries by providing population-based data. Machine learning (ML) has the potential to improve the initial screening of hip devices and the early identification of prosthesis outliers. This study investigated the effectiveness of using random survival forest (RSF) and Cox regression models to account for patient and device confounding factors. By analysing data from 213 hip components and over 163,000 procedures registered with the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), this study compared the effectiveness of ML methods to the AOANJRR’s standard approach. The results revealed that RSF shows promise as a supplementary approach within the hip community, though further investigations are still necessary. View this paper