Machine learning monitoring for laser osteotomy

This work proposes a new online monitoring method for an assistance during laser osteotomy. The method allows differentiating the type of ablated tissue and the applied dose of laser energy. The setup analyzes the laser‐induced acoustic emission, detected by an airborne microphone sensor. The analysis of the acoustic signals is carried out using a machine learning algorithm that is pre‐trained in a supervised manner. The efficiency of the method is experimentally evaluated with several types of tissues, which are: skin, fat, muscle, and bone. Several cutting‐edge machine learning frameworks are tested for the comparison with the resulting classification accuracy in the range of 84–99%. It is shown that the datasets for the training of the machine learning algorithms are easy to collect in real‐life conditions. In the future, this method could assist the doctors during laser osteotomy, minimizing the damage of the nearby healthy tissues and provide cleaner pathologic tissue removal. We investigated the use of supervised machine learning and acoustic emission for in situ monitoring for laser osteotomy. The experiments were conducted on skin, fat, muscle, and bone samples under both wet and dry conditions. We classified with high accuracy in a two sequential operations the sample type and laser dose. Hence, our approach is a promising solution to monitor in situ and in real‐time the laser osteotomy process.