Smart bone plates can monitor fracture healing

One Sentence SummaryElectrical impedance measurements using microscale sensors implanted in two mouse fracture models tracked longitudinal differences between individual mice with proper healing and mice experiencing poor healing, laying the groundwork for translation to the clinic through integration into fracture fixation implants (i.e. instrumented bone plates). AbstractThere are currently no standardized methods for monitoring fracture healing. While histological studies can clearly identify the tissues found in the four stages of repair, in practice surgeons rely on X-ray, which is only useful at later stages of healing after mineralization has occurred. As electrical impedance spectroscopy (EIS) has previously been used to distinguish tissue types during healing, we hypothesized that microscale sensors embedded in the fracture callus could track the changing tissue with high sensitivity. Using in vivo mouse fracture models, we present the first evidence that microscale instrumented implants provide a route for post-operative fracture monitoring. In this study, we implanted sensors in mouse long bone fractures fixed with either external fixators or bone plates. EIS measurements taken across two electrodes implanted in the fracture gap were able to track longitudinal differences between individual mice with proper healing and mice experiencing poor healing. We additionally present an equivalent circuit model that combines the EIS data in order to classify healing states of fractures. Lastly, we show that EIS measures are strongly correlated with standard μCT measures of healing and that these correlations validate clinically-relevant operating frequencies for implementation of this technique. The data from these two models demonstrate that this technique can be translated to the clinic through integration into current fracture management strategies such as bone plating, providing physicians with quantitative information about the state of a fracture to guide clinical decision-making for patients.