Leg-Length Discrepancy Variability on Standard Anteroposterior Pelvis Radiographs: An Analysis Using Deep Learning Measurements

• Published in: The Journal of arthroplasty 2023-10, Vol.38 (10), p.2017-2023.e3
• Main Authors: Jang, Seong Jun, Kunze, Kyle N., Bornes, Troy D., Anderson, Christopher G., Mayman, David J., Jerabek, Seth A., Vigdorchik, Jonathan M., Sculco, Peter K.
• Format: Article
• Language: English
• Subjects: artificial intelligence; deep learning; leg-length discrepancy; osteoarthritis; pelvis
• ISSN: 0883-5403; 1532-8406
• DOI: 10.1016/j.arth.2023.03.006

Leg-length discrepancy (LLD) is a critical factor in component selection and placement for total hip arthroplasty. However, LLD radiographic measurements are subject to variation based on the femoral/pelvic landmarks chosen. This study leveraged deep learning (DL) to automate LLD measurements on pelvis radiographs and compared LLD based on several anatomically distinct landmarks. Patients who had baseline anteroposterior pelvis radiographs from the Osteoarthritis Initiative were included. A DL algorithm was created to identify LLD-relevant landmarks (ie, teardrop (TD), obturator foramen, ischial tuberosity, greater and lesser trochanters) and measure LLD accurately using six landmark combinations. The algorithm was then applied to automate LLD measurements in the entire cohort of patients. Interclass correlation coefficients (ICC) were calculated to assess agreement between different LLD methods. The DL algorithm measurements were first validated in an independent cohort for all six LLD methods (ICC = 0.73–0.98). Images from 3,689 patients (22,134 LLD measurements) were measured in 133 minutes. When using the TD and lesser trochanter landmarks as the standard LLD method, only measuring LLD using the TD and greater trochanter conferred acceptable agreement (ICC = 0.72). When comparing all six LLD methods for agreement, no combination had an ICC>0.90. Only two (13%) combinations had an ICC>0.75 and eight (53%) combinations had a poor ICC (