Deep-Learning Approach for Tissue Classification using Acoustic Waves during Ablation with an Er:YAG Laser (Updated)

• URL: http://arxiv.org/abs/2406.14570v2
• Date: Mon, 24 Jun 2024 09:25:33 GMT
• Title: Deep-Learning Approach for Tissue Classification using Acoustic Waves during Ablation with an Er:YAG Laser (Updated)
• Authors: Carlo Seppi, Philippe C. Cattin,
• Abstract summary: A reliable feedback system is crucial during laser surgery to prevent damage to surrounding tissues. We propose a tissue classification method analyzing acoustic waves generated during laser ablation.
• Score: 0.7892577704654171
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Today's mechanical tools for bone cutting (osteotomy) cause mechanical trauma that prolongs the healing process. Medical device manufacturers aim to minimize this trauma, with minimally invasive surgery using laser cutting as one innovation. This method ablates tissue using laser light instead of mechanical tools, reducing post-surgery healing time. A reliable feedback system is crucial during laser surgery to prevent damage to surrounding tissues. We propose a tissue classification method analyzing acoustic waves generated during laser ablation, demonstrating its applicability in an ex-vivo experiment. The ablation process with a microsecond pulsed Er:YAG laser produces acoustic waves, acquired with an air-coupled transducer. These waves were used to classify five porcine tissue types: hard bone, soft bone, muscle, fat, and skin. For automated tissue classification, we compared five Neural Network (NN) approaches: a one-dimensional Convolutional Neural Network (CNN) with time-dependent input, a Fully-connected Neural Network (FcNN) with either the frequency spectrum or principal components of the frequency spectrum as input, and a combination of a CNN and an FcNN with time-dependent data and its frequency spectrum as input. Consecutive acoustic waves were used to improve classification accuracy. Grad-Cam identified the activation map of the frequencies, showing low frequencies as the most important for this task. Our results indicated that combining time-dependent data with its frequency spectrum achieved the highest classification accuracy (65.5%-75.5%). We also found that using the frequency spectrum alone was sufficient, with no additional benefit from applying Principal Components Analysis (PCA).

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