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Related Experiment Video

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

Hervé Nguendon Kenhagho1, Ferda Canbaz1, Tomas E Gomez Alvarez-Arenas2

  • 1Biomedical Laser and Optics Group, Department of Biomedical Engineering, University of Basel, Gewerbestrasse 14, Allschwil, 4123, Switzerland.

Lasers in Surgery and Medicine
|July 3, 2020
PubMed
Summary

Laser osteotomy offers advantages over mechanical tools. This study shows artificial neural networks (ANN) can accurately classify bone and soft tissues using laser-induced acoustic shock waves (ASWs) for real-time feedback.

Keywords:
acoustic shock signalartificial network machinelaser ablationprincipal component analysissupport vector machinetissue classification

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Area of Science:

  • Biomedical Engineering
  • Surgical Technology
  • Acoustics

Background:

  • Laser osteotomy presents advantages over mechanical bone cutting, including improved healing and contactless interaction.
  • Real-time tissue classification is crucial for optimizing laser bone cutting applications.

Purpose of the Study:

  • To develop and evaluate a real-time tissue classification system for laser osteotomy.
  • To compare the efficacy of different machine learning algorithms for tissue differentiation based on laser-induced acoustic shock waves (ASWs).

Main Methods:

  • Five tissue types (hard bone, soft bone, muscle, fat, skin) from porcine femurs were ablated using Nd:YAG and Er:YAG lasers.
  • Laser-induced acoustic shock waves (ASWs) were recorded using an air-coupled transducer.
  • Principal Component Analysis (PCA) was used for data reduction, followed by classification using Artificial Neural Network (ANN) and Support Vector Machines (SVM).

Main Results:

  • The Artificial Neural Network (ANN) demonstrated the highest classification accuracy, with average errors of 5.01% for Nd:YAG and 9.12% for Er:YAG lasers.
  • Gaussian-SVM outperformed quadratic-SVM, achieving average errors of 15.17% (Nd:YAG) and 16.85% (Er:YAG).
  • Quadratic-SVM showed the lowest performance, with errors up to 69.96%.

Conclusions:

  • Artificial Neural Networks (ANN) are highly effective for real-time tissue classification during laser osteotomy.
  • The developed system using ASWs and ANN shows promise for enhancing the precision and safety of laser bone cutting procedures.