  Plain radiography is widely used to detect mechanical loosening of total hip
replacement (THR) implants. Currently, radiographs are assessed manually by
medical professionals, which may be prone to poor inter and intra observer
reliability and low accuracy. Furthermore, manual detection of mechanical
loosening of THR implants requires experienced clinicians who might not always
be readily available, potentially resulting in delayed diagnosis. In this
study, we present a novel, fully automatic and interpretable approach to detect
mechanical loosening of THR implants from plain radiographs using deep
convolutional neural network (CNN). We trained a CNN on 40 patients
anteroposterior hip x rays using five fold cross validation and compared its
performance with a high volume board certified orthopaedic surgeon (AFC). To
increase the confidence in the machine outcome, we also implemented saliency
maps to visualize where the CNN looked at to make a diagnosis. CNN outperformed
the orthopaedic surgeon in diagnosing mechanical loosening of THR implants
achieving significantly higher sensitively (0.94) than the orthopaedic surgeon
(0.53) with the same specificity (0.96). The saliency maps showed that the CNN
looked at clinically relevant features to make a diagnosis. Such CNNs can be
used for automatic radiologic assessment of mechanical loosening of THR
implants to supplement the practitioners decision making process, increasing
their diagnostic accuracy, and freeing them to engage in more patient centric
care.
