Machine Learning-Assisted Equivalent Circuit Characterization for Electrical Impedance Spectroscopy Measurements of Bone Fractures

Our paper titled ”Machine Learning-Assisted Equivalent Circuit Characterization for Electrical Impedance Spectroscopy Measurements of Bone Fractures” has been accepted by IEEE Transactions on Instrumentation and Measurement and is now available for early access: Article

In this research, we addressed the challenges in the interpretation of Electrical Impedance Spectroscopy (EIS) measurements for fracture healing monitoring leveraging a distributed-element equivalent circuit model and machine learning (ML) to enhance the accuracy and reliability of fitting measured data.