IEEE Trans Ultrason Ferroelectr Freq Control. 2019 Sep;66(9):1435-1443. doi: 10.1109/TUFFC.2019.2923696. Epub 2019 Jun 19.
Laser osteotomy offers a way to make precise and less traumatic cuts smaller than conventional mechanical bone surgery tools. To fully exploit the advantages of laser osteotomy over conventional osteotomy, real-time feedback to differentiate the hard bone from the surrounding soft tissues is desired. In this study, we differentiated various tissue types-hard and soft bone, fat, muscle, and skin tissues from five proximal and distal fresh porcine femurs-based on cutting sounds. For laser ablation, an Nd:YAG laser was used to create ten craters on the surface of each proximal and distal femurs. For sound recording, the probing beam of a Mach-Zehnder interferometer was placed 5 cm away from each ablation site. For offline tissue differentiation, we investigated the measurements by looking at the amplitude frequency band between 0.83 and 1.25 MHz, which provides the least average classification error. Then, we used principal component analysis to reduce the dimensionality and the 95% confidence ellipsoid (Mahalanobis distance) method to differentiate between tissues based on the acoustic shock wave. A set of 14 400 data points, measured from ten craters in four proximal and distal femurs, was used as "training data," while a set of 3600 data points from ten craters in the remaining proximal and distal femurs was considered as "testing data." As is seen in the confusion matrix, the experimental-based scores of hard and soft bones, fat, muscles, and skin yielded average classification errors (with leave-one-out cross validation) of 0.11%, 57.69%, 0.06%, 0.14%, and 2.92%, respectively. The results of this study demonstrate a promising technique for differentiating tissues during laser osteotomy.
激光截骨术提供了一种方法，可以比传统的机械骨手术工具更精确、创伤更小地进行切割。为了充分利用激光截骨术相对于传统截骨术的优势，需要实时反馈来区分硬骨和周围软组织。在这项研究中，我们基于切割声音从五个近端和远端新鲜猪股骨中区分了各种组织类型——硬骨和软组织、脂肪、肌肉和皮肤组织。对于激光烧蚀，使用 Nd:YAG 激光在每个近端和远端股骨的表面上创建十个凹坑。对于声音记录，马赫-曾德尔干涉仪的探测光束放置在每个烧蚀点 5 厘米处。对于离线组织分化，我们研究了通过观察 0.83 到 1.25MHz 之间的振幅频带的测量结果，该频带提供了最小的平均分类错误。然后，我们使用主成分分析来降低维度，使用 95%置信椭圆体（马氏距离）方法根据声冲击波来区分组织。从四个近端和远端股骨中的十个凹坑测量的一组 14400 个数据点被用作“训练数据”，而剩余近端和远端股骨中的十个凹坑的一组 3600 个数据点被视为“测试数据”。如混淆矩阵所示，硬骨和软组织、脂肪、肌肉和皮肤的实验得分的平均分类错误（采用留一法交叉验证）分别为 0.11%、57.69%、0.06%、0.14%和 2.92%。这项研究的结果表明，这是一种在激光截骨术中区分组织的有前途的技术。
