Zonal level Implant Loosening Detection from Hip X-ray using a Multi-stage Deep Learning Method

Hip arthroplasty is a surgical procedure that involves the replacement of a patient's hip joint with a prosthetic implant. While these implants are initially effective, they may eventually fail and necessitate revision surgery. It is important to identify the 3 Charnley and 7 Gruen zones around the implant and then identify the zone-wise radiolucency which indicates loosening for effective pre- and post-operative planning. Despite the importance of zones, there is a lack of automation attempts in this field. In this work, we have proposed a 3-stage algorithm that detects the sanity of the image for diagnosis, then segments the implant regions into the zones, and then identifies radiolucency within the zones. We have demonstrated a 94% accuracy for Fit/Not Fit segregation, a 0.95 dice score for our zonal segmentation, and a 98% overall loosening accuracy. Obtaining an average dice score of 0.92 in the segmentation of zones and 0.93 accuracy on loosening detection on a blind dataset indicates the robustness of the proposed algorithm.Clinical relevanceThe detection of loosening of Joint replacement is an indicator of the prosthesis failing and needing a revision. The extent of loosening along the zones helps to determine the level of difficulty and the type of implant that would need to be kept ready for revision arthroplasty. Our work provides these critical information to the surgeons and helps them towards better preplanning of revision surgery.