Abstract
Background
Dislocation is a common complication following total hip arthroplasty (THA), and accounts for a high percentage of subsequent revisions. The purpose of this study was to develop a convolutional neural network (CNN) model to identify patients at high risk for dislocation based on postoperative anteroposterior (AP) pelvis radiographs.
Methods
We retrospectively evaluated radiographs for a cohort of 13,970 primary THAs with 374 dislocations over 5 years of follow-up. Overall, 1,490 radiographs from dislocated and 91,094 from non-dislocated THAs were included in the analysis. A CNN object detection model (YOLO-V3) was trained to crop the images by centering on the femoral head. A ResNet18 classifier was trained to predict subsequent hip dislocation from the cropped imaging. The ResNet18 classifier was initialized with ImageNet weights and trained using FastAI (V1.0) running on PyTorch. The training was run for 15 epochs using ten-fold cross validation, data oversampling and augmentation.
Results
The hip dislocation prediction classifier achieved the following mean performance: accuracy= 49.5(±4.1)%, sensitivity= 89.0(±2.2)%, specificity= 48.8(±4.2)%, positive predictive value= 3.3(±0.3)%, negative predictive value= 99.5(±0.1)%, and area under the receiver operating characteristic curve= 76.7(±3.6)%. Saliency maps demonstrated that the model placed the greatest emphasis on the femoral head and acetabular component.
Conclusions
Existing prediction methods fail to identify patients at high risk of dislocation following THA. Our prediction model has high sensitivity and negative predictive value. Therefore, it can be helpful in rapid assessment of risk for dislocation following THA. The model further suggests radiographic locations which may be important in understanding the etiology of prosthesis dislocation.
