Optoacoustic Tissue Classification for Laser Osteotomes Using Mahalanobis Distance-Based Method

Abstract

The use of lasers for bone cutting holds many advantages over mechanical tools, including more functional cuts, contactless interaction and faster wound healing. To avoid undesirable tissue damage, a method to classify the tissue being cut is needed. We classified four tissue types—hard and soft bone, muscle and fat from a proximal and a distal fresh porcine femur—by measuring acoustic shock waves generated using an air-coupled transducer during the ablation process. A nanosecond Nd:YAG laser at 532 nm and a microsecond Er:YAG laser at 2940 nm were used to create ten craters on the surface of each tissue type. We performed the Principal Component Analysis (PCA) combined with the Mahalanobis distance-based method to classify tissue types. A set of 2520 data points (or 840 average of three spectra) measured from the first seven craters in one proximal and distal femurs was used as “training data”, while a set of 1080 data points (or 360 average three spectra) measured from last three craters in the remaining proximal and distal femur was considered as “testing data” for both lasers. It was possible to classify each tissue, with an average classification error for all tissues of 7.98% and 36.88%, during laser ablation with the Nd:YAG and Er:YAG, respectively. However using the Er:YAG, it was challenging to classify between soft tissue types. These results show that the Mahalanobis method could be used as feedback for laser osteotomes.