Our purpose is to assess if bone drilling sounds could be used to automatically distinguish between hard- and soft-bone tissues. Such a capability could be of value in a variety of surgical procedures involving drilling through bone. We acquired sound signals from six bovine tibial bones that were being drilled with a surgical drill. We investigated various classifiers including logistic regression, support vector machine (SVM), random forest (RF), and hidden Markov model. We explored different time and frequency features, and considered two training/testing scenarios: leave-one bone-out (LOBO) and bone-specific (BSP). Moreover, we conducted a survey of practicing surgeons to provide a baseline measure for assessing whether our proposed algorithm could improve the ability of surgeons to identify hard- and soft-bone tissues based on drilling sounds. The average accuracies for LOBO and BSP were 79.1% and 84.3%, respectively. In both scenarios, the feature that resulted in the highest accuracy of classification was wavelet packet transform coefficients. Moreover, RF and SVM produced the highest accuracies for LOBO and BSP, respectively. In the survey, the surgeons were unsure on 29.2% of questions, and had an average accuracy of 51.4% on their judged answers. In conclusion, the drilling in hard and soft bones could be automatically identified with good accuracy based on the drilling sounds. Several of the algorithms tested were generalizable across specimens and their accuracy significantly exceeded surgeons' performance. The significance is that these algorithms can potentially be used to reduce challenges associated with bone-drilling procedures.

Dental restoration begins with removing carries and affected tissues with air-turbine rotary cutting handpieces, and later restoring the lost tissues with appropriate restorative materials to retain the functionality. Most restoration materials eventually fail as they age and need to be replaced. One of the difficulties in replacing failing restorations is discerning the boundary of restorative materials, which causes inadvertent removal of healthy tooth layers. Developing an objective and sensor-based method is a promising approach to monitor dental restorative operations and to prevent excessive tooth losses. This paper has analyzed cutting sounds of an air-turbine handpiece to discriminate between tooth layers and two commonly used restorative materials, amalgam and composite. Support vector machines were employed for classification, and the averaged short-time Fourier transform coefficients were selected as the features. The classifier performance was evaluated from different aspects such as the number of features, feature scaling methods, classification schemes, and utilized kernels. The total classification accuracies were 89% and 92% for cases included composite and amalgam materials, respectively. The obtained results indicated the feasibility and effectiveness of the proposed method.

This paper introduces a monitoring scheme for discerning the boundary of the tooth in dental operations. In this scheme, tooth structures and dental fillings were discriminated based on their cutting sounds. Support vector machines were employed for classification; and averaged short time Fourier transform coefficients were selected as the features. The results confirmed capability and feasibility of the proposed scheme.