Abstract
As laser osteotomy offers precise and small cuts with less trauma compared to conventional mechanical bone surgery. To fully exploit the advantages of laserosteotomes over conventional osteotomes, real-time feedback to differentiate hard bone from surrounding soft tissues is desired. In this study, we differentiated various tissue types - hard bone, fat and muscle tissue from an upper and lower fresh porcine thigh - based on cutting sounds. For laser ablation, an Nd:YAG laser was used to create ten craters on the surface of each upper and lower thigh. For sound recording, the probing beam of a Mach-Zehnder interferometer was placed 5cm away from each ablation site. For offline tissue differentiation, we investigated the measurements by looking at the amplitude of the spectrum. Then, we used Principle Component Analysis (PCA) to reduce the dimensionally and the 95% confidence ellipsoid (Mahalanobis distance) method to differentiate between tissues based on the acoustic shock wave. A set of 2520 data points, measured from the first seven craters of the upper and lower thigh, was used as ‘training data’ a set of 1080 data points from the last three craters was considered as ‘testing data’. As seen in the confusion matrix, the experimental data from hard bone, fat and muscle yielded error rates of 0.46%, 0.19% and 1.30%, respectively. Preliminary results of this study demonstrate a promising technique for differentiating bone, fat and muscle tissues during laser surgery.
Published Version
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