Correlation between AI algorithm generated radiographic hip measurements for hip dysplasia patients and internal...

BackgroundHip dysplasia (HD) involves abnormal acetabular development, resulting in reduced femoral head coverage, labral tears, and cartilage injury. Machine learning (AI) advancements have enabled reproducible radiographic measurements for HD, such as lateral center edge angle (LCEA), Tonnis, and extrusion index. Moreover, incorporating 3D magnetic resonance imaging (MRI) alongside 2D MRI enhances diagnostic capabilities.PurposeTo correlate advanced MRI-assessed labral and cartilage injuries with validated AI-generated radiographic measurements.Material and MethodsThis study enrolled 139 patients (age range = 16-68 years) with HD, comprising a total of 156 hips. All patients had 2D and 3D MRI scans, four-view X-rays, and AI-generated radiographic measurements using a commercial AI program that utilized bony landmarks to generate measurements. Labral reconstructions were obtained for each hip, and a multi-reader study was conducted. Inter-reader (ICC) analysis and Spearman correlations were calculated.ResultsThe predominant location for the largest labral tear was anterosuperior (133/156, 90%), and paralabral cysts were observed in 53/156 (34%) cases. No statistically significant correlations were found between the length of labral tears and radiographic measurements. However, statistically significant correlations were observed between paralabral cysts and femoral head coverage, extrusion index, LCEA, and Tonnis measurements.ConclusionAI-generated radiographic measurements in HD exhibited weak correlations with advanced MRI findings, likely due to the condition's complex pathophysiology.