A novel concept of an acoustic ultrasound wearable for early detection of implant failure.

Mechanical failure of medical implants, especially in orthopedic poses a significant burden to the patients and healthcare system. The majority of the implant failures are diagnosed at very late stages and are of mechanical causes. This makes the diagnosis and screening of implant failure very challenging. There have been several attempts for development of new implants and screening methods to address this issue; however, the majority of these methods focus on development of new implants or material and cannot satisfy the needs of the patients that have already been operated on. In this work we are introducing a novel screening method and investigate the feasibility of using low-intensity, low-frequency ultrasound acoustic waves for understanding of interfacial implant defects through computational simulation. In this method, we simultaneously apply and sense acoustic waves. COMSOL simulations proved the correlation between implant health condition, severity, and location of defects with measured acoustic signal. Moreover, we show that machine learning not only can detect and classify failure types, it can also assess the severity of the defects. We believe that this work can be used as a proof of concept to rationalize the development of non-invasive screening acoustic wearables for early detection of implant failure in patients with orthopedic implants.