In The Journal of arthroplasty ; h5-index 65.0
BACKGROUND : 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.
PATIENT AND METHODS : Patients who had baseline antero-posterior pelvis radiographs from the Osteoarthritis Initiative were included. A DL algorithm was created to identify LLD-relevant landmarks (i.e., teardrop, 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. Inter-class correlation coefficients (ICC) were calculated to assess agreement between different LLD methods.
RESULTS : 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 teardrop and lesser trochanter landmarks as the standard LLD method, only measuring LLD using the teardrop 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 (<0.50).
CONCLUSION : We leveraged DL to automate LLD measurements in a large patient cohort and found considerable variation in LLD based on the pelvic/femoral landmark selection. This emphasizes the need for the standardization of landmarks for both research and surgical planning.
Jang Seong Jun, Kunze Kyle N, Bornes Troy, Anderson Christopher G, Mayman David J, Jerabek Seth A, Vigdorchik Jonathan M, Sculco Peter K
2023-Mar-08
Pelvis, artificial intelligence, deep learning, leg-length discrepancy, osteoarthritis
