Using acoustic feedback and analyses for removal of bone cement during revision knee replacement surgeries.

Abstract

The purpose of this study was to analyze the scraping sounds generated during revision knee replacement surgeries to discriminate between the inner cortical bone and the cement, with the goal of minimizing bone removal and increasing the structural integrity of the revision. We prepared seven porcine femurs by partially filling them with bone cement, and recorded scraping sounds produced by a surgical scraping tool. We used a hierarchical machine learning approach to first detect a contact and then classify it as either bone or cement. This approach was based on a Support Vector Machine learning algorithm that was fed with temporal and spectral features of the sounds. A Leave-One-Bone-Out validation method was used to assess the performance of the proposed method. The average recall for the noncontact, bone, and cement classes was 98%, 75%, and 72%, respectively. The corresponding precision for the respective classes was 99%, 67%, and 61%. The scraping sound that is generated during revision replacement surgeries carries significant information about the material that is being scraped. Such information can be extracted using a supervised machine learning algorithm. The scraping sound produced during revision replacement procedures can potentially be used to enhance cement removal during knee revision surgery. Future work will assess whether such monitoring can increase the structural integrity of the revision.