Intelligent femtosecond laser bone drilling via online monitoring and machine learning

Abstract

In conventional spinal surgeries, mechanical and thermal injuries frequently usually arise due to improper handling, giving rise to a range of complications including infection, poor wound healing and bleeding. Femtosecond laser ablation offers a promising approach owing to high precision and low thermal damage. In this study, an intelligent femtosecond laser drilling method of human spinal bones has been proposed, and a machine learning method has been employed to determine the optimal laser processing window, ensuring high-quality outcomes. A neural network model has been developed to predict drilling quality, achieving an impressive accuracy rate exceeding 98 %, along with precision and recall rates of 100 % and 92.86 %, respectively. To further monitor the process, a fiber spectrometer and a thermal camera has been employed to monitor the focal status and bone temperature during laser processing to make sure the drilling is in a focal position and temperature in safe range. Subsequently, the drilling efficiency has been predicted using another neural network model within high-quality processing window for the maximum ablation processing parameter. The current research has demonstrated a direct, non-destructive and efficient method for intelligent laser spinal drilling.