Classifying hard and soft bone tissues using drilling sounds.

The purpose of this study was to investigate if the sounds generated during bone drilling could be used to classify between hard (cortical) and soft (cancellous) tissues. Bone drilling is performed in many surgical procedures throughout the world. Inadvertent deviation from the correct drill direction may result in injuries to sensitive anatomical structures such as nerve and vessels. Therefore, to increase the safety of such procedures, it is necessary to identify different bone tissues. The cortical and cancellous tissues of six bovine tibia pieces were drilled and the generated sounds were recorded. Each record was analyzed in different frequency regions based on the spectrograms. From each region, short-time Fourier transform (STFT) coefficients were computed and averaged accordingly to obtain n bins. The total bins of all frequency regions were chosen as the features. A support vector machine (SVM) algorithm was selected for classification and the performance was evaluated in two training/testing scenarios: leave one bone out (LOBO) and bone specific (BSP). The average total accuracy on the testing data was 70.9% and 83% for LOBO and BSP respectively. The results indicated that the drilling sounds obtained from various bone pieces could be used to develop a classification model that had promising performance on identifying hard and soft components of a new bone piece.