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At an estimated cost of $8 billion annually in the United States, revisionsurgeries to total joint replacements represent a substantial financial burdento the health care system. Fixation failures, such as implant loosening, wear,and mechanical instability of the poly(methyl methacrylate) (PMMA) cement,which bonds the implant to the bone, are the main causes of long-term implantfailure. Early and accurate diagnosis of cement failure is critical fordeveloping novel therapeutic strategies and reducing the high risk of amisjudged revision. Unfortunately, prevailing imaging modalities, notably plainradiographs, struggle to detect the precursors of implant failure and are ofteninterpreted incorrectly. Our prior work has shown that the modification of PMMAbone cement with low concentrations of conductive fillers makes itpiezoresistive and therefore self-sensing such that when combined with aconductivity imaging modality, such as electrical impedance tomography (EIT),it is possible to monitor load transfer across the PMMA using cost-effective,physiologically benign, and real-time electrical measurements. Herein, weexpand upon these results by integrating machine learning techniques with EIT.We survey different machine learning algorithms and principal componentanalysis for application to this problem, including neural networks on voltagereadings of an EIT phantom for tracking position of a sample, specifying defectlocation, and classifying defect types. Our results show advantage of neuralnetwork with more than 91.9 %, 95.5 %, and 98 % accuracy in interpreting EITsignals for location tracking, specifying defect location, and defectclassification respectively. These preliminary results show that thecombination of smart materials, EIT, and machine learning may be a powerfultool for diagnosing the origin and evolution of failure in joint replacement.

Is Machine Learning Able to Detect and Classify Failure in Piezoresistive Bone Cement Based on Electrical Signals?