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Validation of AI-driven measurements for hip morphology assessment
Louis Lassalle1, Nor-Eddine Regnard1, Marion Durteste2
1Réseau Imagerie Sud Francilien, Lieusaint, France; Ramsay Santé, Clinique du Mousseau, Evry, France; Gleamer, Paris, France.
Rationale And Objectives:
Accurate assessment of hip morphology is crucial for the diagnosis and management of hip pathologies. Traditional manual measurements are prone to mistakes and inter- and intra-reader variability. Artificial intelligence (AI) could mitigate such issues by providing accurate and reproducible measurements. The aim of this study was to compare the performance of BoneMetrics (Gleamer, Paris, France) in measuring pelvic and hip parameters on anteroposterior (AP) and false profile radiographs to expert manual measurements.
Materials And Methods:
This retrospective study included AP and false profile pelvic radiographs collected from private practices in France. Pelvic and hip measurements included the femoral neck shaft angle, lateral center edge angle, acetabular roof angle, pelvic obliquity, and vertical center anterior angle. AI measurements were compared to a ground truth established by two expert radiologists. Performance metrics included mean absolute error (MAE), Bland-Altman analysis, and intraclass correlation coefficients (ICC).
Results:
AI measurements were performed on AP views from 88 patients and on false profile views from 60 patients. They demonstrated high accuracy, with MAE values inferior to 0.5 mm for pelvic obliquity and inferior to 4.2° for all pelvic angles on both views. ICC values indicated good to excellent agreement between AI measurements and the ground truth (0.78-0.99). Notably, no significant differences were found in AI measurement accuracy between patients with normal and abnormal acetabular coverage.
Conclusion:
The application of AI in measuring pelvic and hip parameters on AP and false profile radiographs demonstrates promising outcomes. The results reveal that these AI-powered measurements provide accurate estimations and show strong agreement with expert manual measurements. Ultimately, the use of AI has the potential to enhance the reproducibility of measurements as part of comprehensive hip assessments, thereby improving diagnostic accuracy.
