UC Riverside

Continuous quantitative monitoring of the change in mineral content during the bone healing process is crucial for efficient clinical treatment. Current radiography-based modalities, however, pose various technological, medical, and economical challenges such as low sensitivity, radiation exposure risk, and high cost/instrument accessibility. In this regard, an analytical approach utilizing electrochemical impedance spectroscopy (EIS) assisted by machine learning algorithms is developed to quantitatively characterize the physico-electrochemical properties of the bone, in response to the changes in the bone mineral contents. The system is designed and validated following the process of impedance data measurement, equivalent circuit model designing, machine learning algorithm optimization, and data training and testing. Overall, the systematic machine learning-based classification utilizing the combination of EIS measurements and electrical circuit modeling offers a means to accurately monitor the status of the bone healing process.