Identifying the design of a failed implant is a key step in preoperative
planning of revision total joint arthroplasty. Manual identification of the
implant design from radiographic images is time consuming and prone to error.
Failure to identify the implant design preoperatively can lead to increased
operating room time, more complex surgery, increased blood loss, increased bone
loss, increased recovery time, and overall increased healthcare costs. In this
study, we present a novel, fully automatic and interpretable approach to
identify the design of total hip replacement (THR) implants from plain
radiographs using deep convolutional neural network (CNN). CNN achieved 100%
accuracy in identification of three commonly used THR implant designs. Such CNN
can be used to automatically identify the design of a failed THR implant
preoperatively in just a few seconds, saving time and improving the
identification accuracy. This can potentially improve patient outcomes, free
practitioners time, and reduce healthcare costs.