Exploring Model-based Failure Prediction of Passive Bio-electro-mechanical Implants

A range of medical issues are treated by simple biomechanical implants to regulate fluid pressure and flow (e.g. valves, shunts). With moving parts in fluid, these implants are vulnerable to biological failures (infection, migration), mechanical failures (clogging, cracking), and parametric failures (change in flow resistance, cracking pressure). Existing biomechanical implants only show failure by clinical symptoms, which may be catastrophic. A means to better observe device behavior and predict failure is necessary. We explore merging biomechanical implants with low-footprint passive electronics, creating bio-electro-mechanical (BEM) devices and thereby allowing external monitoring. Passive feedback signals (RF backscatter) may be interpreted by a model to extract flow parameters and predict failure. A model may be trained by benchtop testing, to correlate direct measurements (flow, pressure) with passive device signals. Benchtop failure simulation (accelerated aging, simulated biofouling) may better train the model for failure prediction. This paper uses long-term pressure/flow testing data from a simple biomechanical device (hydrogel valve for hydrocephalus) as a test case for extracting predictive signals of imminent device failure.