Ultrasounds for Biological Applications and Materials Science

Our last paper: “Machine Learning‐Based Optoacoustic Tissue Classification Method for Laser Osteotomes Using an Air‐Coupled Transducer” in Lasers in Surgery and Medicine Journal.


In collaboration with colleagues from:

Biomedical Laser and Optics Group, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, Allschwil, 4123 Switzerland

Brain Ischemia and Regeneration, Department of Biomedicine, University of Basel, University Hospital of Basel, Basel, 4031 Switzerland

Department of Biomedical Engineering, Center for Medical Image Analysis and Navigation, University of Basel, Gewerbestrasse 14, Allschwil, 4123 Switzerland


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.


The ANN performed best for both lasers, with an average classification error for all tissues of 5.01 ± 5.06% and 9.12 ± 3.39%, using the Nd:YAG and Er:YAG lasers, respectively. Then, the Gaussian‐SVM performed better than the quadratic SVM during the cutting with both lasers. The Gaussian‐SVM yielded average classification errors of 15.17 ± 13.12% and 16.85 ± 7.59%, using the Nd:YAG and Er:YAG lasers, respectively. The worst performance was achieved with the quadratic‐SVM with a classification error of 50.34 ± 35.04% and 69.96 ± 25.49%, using the Nd:YAG and Er:YAG lasers.


We foresee using the ANN to differentiate tissues in real‐time during laser osteotomy. Lasers Surg. Med. © 2020 Wiley Periodicals LLC


Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s


This entry was posted on August 5, 2020 by in Uncategorized.
0034 915618806-058
Skype: usbiomat_csic
wordpress stats plugin
%d bloggers like this: