Automated Femoral, Acetabular, and Global Offset Measurements on Pelvis Radiograph Using Deep Learning.
Authors
Affiliations (3)
Affiliations (3)
- Department of Orthopedic Surgery, Hospital for Special Surgery, New York, NY, USA.
- Department of Orthopedic Surgery, Warren Alpert Medical School of Brown University, Providence, RI, USA.
- Adult Reconstruction and Joint Replacement Service, Hospital for Special Surgery, New York, NY, USA.
Abstract
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. 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. 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. 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.