Abstract
Corrosion at the modular head-neck taper interface of total and hemiarthroplasty hip implants (trunnionosis) is a cause of implant failure and clinical concern. The Goldberg corrosion scoring method is considered the gold standard for observing trunnionosis, but it is labor-intensive to perform. This limits the quantity of implants retrieval studies typically analyze. Machine learning, particularly convolutional neural networks, have been used in various medical imaging applications and corrosion detection applications to help reduce repetitive and tedious image identification tasks. 725 retrieved modular femoral stem arthroplasty devices had their trunnion imaged in four positions and scored by an observer. A convolutional neural network was designed and trained from scratch using the images. There were four classes, each representing one of the established Goldberg corrosion classes. The composition of the classes were as follows: class 1 (n = 1228), class 2 (n = 1225), class 3 (n = 335), and class 4 (n = 102). The convolutional neural network utilized a single convolutional layer and RGB coloring. The convolutional neural network was able to distinguish no and mild corrosion (classes 1 and 2) from moderate and severe corrosion (classes 3 and 4) with an accuracy of 98.32%, a class 1 and 2 sensitivity of 0.9881, a class 3 and 4 sensitivity of 0.9556 and an area under the curve of 0.9740. This convolutional neural network may be used as a screening tool to identify retrieved modular hip arthroplasty device trunnions for further study and the presence of moderate and severe corrosion with high reliability, reducing the burden on skilled observers.