Automated Femoral, Acetabular, and Global Offset Measurements on Pelvis Radiograph Using Deep Learning.

Purpose: Offset measurement is critical in total hip arthroplasty (THA) for guiding restoration of native anatomy. However, measurements are time-consuming and measurer-dependent, creating obstacles for large cohort analyses. We aim to create an objective and reliable offset measurement algorithm using deep learning. Materials and Methods: Five hundred radiographs from the Osteoarthritis Initiative (OAI) were segmented with identification of the teardrop, femoral head, implant head, and femoral diaphysis. A U-Net convolutional neural network was trained to identify these landmarks and optimized using the multi-class Dice coefficient metric. Femoral axis and femoral/implant head center of rotation were calculated with the model predictions, and measurements of offset were compared against two trained readers on an independent testing cohort. Results: The optimized model had a Dice coefficient of 0.96 and a foreground mask accuracy of 96.2%. The model measured femoral, acetabular, and global offset on both limbs at a rate of 1.67 sec/image. On an independent cohort (n=90), the intraclass correlation coefficient between readers and the algorithm was 0.86 (95% confidence interval [CI] 0.80-0.91) for femoral offset, 0.87 (95% CI 0.78-0.91) for acetabular offset, and 0.94 (95% CI 0.91-0.96) for global offset. When applied to the entire OAI cohort (n=4,188), all relevant anatomical features (femoral axis, implant/femoral center of rotation, inter-teardrop line) were correctly calculated in 83.0% of images. Conclusion: We report the development of an accurate and rapid offset measurement model using deep learning that can be applied before and after THA. Future work will involve external model validation.