Bone Ablation Depth Estimation From Er:YAG Laser-Generated Acoustic Waves

Carlo Seppi, Antal Huck, Arsham Hamidi, Eva Schnider, Massimiliano Filipozzi, Georg Rauter, Azhar Zam, Philippe C. Cattin

Research output: Contribution to journalArticlepeer-review


Using a laser for cutting bones instead of the traditional saws improves a patient's healing process. Additionally, the laser has the potential to reduce the collateral damage to the surrounding tissue if appropriately applied. This can be achieved by building additional sensing elements besides the laser itself into an endoscope. To this end, we use a microsecond pulsed Erbium-doped Yttrium Aluminium Garnet (Er:YAG) laser to cut bones. During ablation, each pulse emits an acoustic shock wave that is captured by an air-coupled transducer. In our research, we use the data from these acoustic waves to predict the depth of the cut during the ablation process. We use a Neural Network (NN) to estimate the depth, where we use one or multiple consecutive measurements of acoustic waves. The NN outperforms the base-line method that assumes a constant ablation rate with each pulse to predict the depth. The results are evaluated and compared against the ground-truth depth measurements from Optical Coherence Tomography (OCT) images that measure the depth in real-time during the ablation process.

Original languageEnglish (US)
Pages (from-to)126603-126611
Number of pages9
JournalIEEE Access
StatePublished - 2022


  • Acoustic feedback
  • depth control
  • laser ablation
  • neural network

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering
  • Electrical and Electronic Engineering


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