Bone-Implant Osseointegration Monitoring Using Electro-mechanical Impedance Technique and Convolutional Neural Network: A Numerical Study

Accurate quantification of the jawbone-implant interface plays a pivotal role in assessing the mechanical stability of dental implant structures. This study proposes a methodology integrating the electro-mechanical impedance (EMI)-based technique with a deep learning algorithm to autonomously monitor the bone-implant interface during the osseointegration process. We develop a 1D convolutional neural network (1D CNN) model, which automatically processes raw EMI data and extracts optimal features for predicting osseointegration ratios. To validate our approach, we conduct predictive 3D numerical modelling of the PZT-implant-bone system. This model simulates the implant’s EMI response under varying degrees of osseointegration. Next, we employ traditional statistical metrics to monitor osseointegration and discuss their limitations. Finally, we apply the proposed 1D CNN model to predict bone-implant osseointegration rate. We train and test the network using the simulated EMI data with added noise to account for real-world conditions. The results show that the trained model achieves a minimal testing error of just 2.4