Evaluating Specialized CNN Architectures for Hip Implant Loosening Detection on Radiographic Images

This study presents a deep learning approach for detecting cases of aseptic loosening from radiographic images in patients that have undergone hip arthroplasty surgery. Aseptic loosening is a leading cause of implant failure and typically requires manual evaluation by orthopedic surgeons, which can be tedious and error prone. To address this challenge, two specialized convolutional neural network (CNN) architectures, EfficientNetB3 and VGG19, as well as a standard CNN architecture were applied to the task. The three architectures were trained on a publicly available dataset obtained from the Kaggle platform, consisting of 206 anteroposterior radiographic images. The validation performances of the models were compared using the metrics of accuracy, precision, recall, F1-score, and area under receiver operating characteristic curve (AUC). VGG19 architecture achieved superior performance to the other two architectures across all metrics, attaining the highest validation accuracy of 88% and the highest AUC value of 0.91; this was followed by EfficientNetB3 and then the standard CNN. The findings demonstrated the potential of specialized pre-trained CNN architectures, leveraging transfer learning paradigm, in improving the reliability of radiographic analysis, enhancing early diagnosis, and improving patient outcomes in orthopedic radiology.