Radiographic Findings Associated With Mild Hip Dysplasia in 3869 Patients Using a Deep Learning Measurement Tool

• Published in: Arthroplasty today Vol. 28; p. 101398
• Main Authors: Jang, Seong Jun, Driscoll, Daniel A., Anderson, Christopher G., Sokrab, Ruba, Flevas, Dimitrios A., Mayman, David J., Vigdorchik, Jonathan M., Jerabek, Seth A., Sculco, Peter K.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2024; Elsevier
• Subjects: Artificial intelligence; Deep learning; Hip dysplasia; Original Research; Osteoarthritis; Deep learning; Hip dysplasia; Artificial intelligence; Osteoarthritis
• ISSN: 2352-3441; 2352-3441
• DOI: 10.1016/j.artd.2024.101398

Hip dysplasia is considered one of the leading etiologies contributing to hip degeneration and the eventual need for total hip arthroplasty (THA). We validated a deep learning (DL) algorithm to measure angles relevant to hip dysplasia and applied this algorithm to determine the prevalence of dysplasia in a large population based on incremental radiographic cutoffs. Patients from the Osteoarthritis Initiative with anteroposterior pelvis radiographs and without previous THAs were included. A DL algorithm automated 3 angles associated with hip dysplasia: modified lateral center-edge angle (LCEA), Tönnis angle, and modified Sharp angle. The algorithm was validated against manual measurements, and all angles were measured in a cohort of 3869 patients (61.2 ± 9.2 years, 57.1% female). The percentile distributions and prevalence of dysplastic hips were analyzed using each angle. The algorithm had no significant difference (P > .05) in measurements (paired difference: 0.3°-0.7°) against readers and had excellent agreement for dysplasia classification (kappa = 0.78-0.88). In 140 minutes, 23,214 measurements were automated for 3869 patients. LCEA and Sharp angles were higher and the Tönnis angle was lower (P < .01) in females. The dysplastic hip prevalence varied from 2.5% to 20% utilizing the following cutoffs: 17.3°-25.5° (LCEA), 9.4°-15.6° (Tönnis), and 41.3°-45.9° (Sharp). A DL algorithm was developed to measure and classify hips with mild hip dysplasia. The reported prevalence of dysplasia in a large patient cohort was dependent on both the measurement and threshold, with 12.4% of patients having dysplasia radiographic indices indicative of higher THA risk.