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).

通过分析激光消融期间产生的声波，我们提出了一种组织分类方法，并在体外实验中证明了其适用性，最终确定将时间相关数据与其频谱相结合的方法可以实现最高的分类准确性（65.5%-75.5%）。