Machine Learning-Based Optoacoustic Tissue Classification Method for Laser Osteotomes Using an Air-Coupled Transducer

Hervé Nguendon Kenhagho, Ferda Canbaz, Tomas E. Gomez Alvarez-Arenas, Raphael Guzman, Philippe Cattin, Azhar Zam
Lasers Surg Med. 2021 Mar;53(3):377-389

Background and objectives: Using lasers instead of mechanical tools for bone cutting holds many advantages, including functional cuts, contactless interaction, and faster wound healing. To fully exploit the benefits of lasers over conventional mechanical tools, a real-time feedback to classify tissue is proposed.

Study design/materials and methods: In this paper, we simultaneously classified five tissue types-hard and soft bone, muscle, fat, and skin from five proximal and distal fresh porcine femurs-based on the laser-induced acoustic shock waves (ASWs) generated. For laser ablation, a nanosecond frequency-doubled Nd:YAG laser source at 532 nm and a microsecond Er:YAG laser source at 2940 nm were used to create 10 craters on the surface of each proximal and distal femur. Depending on the application, the Nd:YAG or Er:YAG can be used for bone cutting. For ASW recording, an air-coupled transducer was placed 5 cm away from the ablated spot. For tissue classification, we analyzed the measured acoustics by looking at the amplitude-frequency band of 0.11-0.27 and 0.27-0.53 MHz, which provided the least average classification error for Er:YAG and Nd:YAG, respectively. For data reduction, we used the amplitude-frequency band as an input of the principal component analysis (PCA). On the basis of PCA scores, we compared the performance of the artificial neural network (ANN), the quadratic- and Gaussian-support vector machine (SVM) to classify tissue types. A set of 14,400 data points, measured from 10 craters in four proximal and distal femurs, was used as training data, while a set of 3,600 data points from 10 craters in the remaining proximal and distal femur was considered as testing data, for each laser.


This project is part of the MIRACLE (short for Minimally Invasive Robot-Assisted Computer-guided LaserosteotomE) project funded by the Werner Siemens Foundation. The ACTs part was funded by (DPI2016-78876-R-AEI/FEDER, UE) from the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF/FEDER).