Deep learning and conventional hip MRI for the detection of labral and cartilage abnormalities using arthroscopy as standard of reference.

Abstract

To evaluate the performance of high-resolution deep learning-based hip MR imaging (CSAI) compared to standard-resolution compressed sense (CS) sequences using hip arthroscopy as standard of reference. Thirty-two patients (mean age, 37.5 years (± 11.7), 24 men) with femoroacetabular impingement syndrome underwent 3-T MR imaging prior to hip arthroscopy. Coronal and sagittal intermediate-weighted TSE sequences with fat saturation were obtained using CS (0.6 × 0.8 mm) and high-resolution CSAI (0.3 × 0.4 mm), with 3 mm slice thickness and similar acquisition times (3:55-4:12 min). MR scans were independently assessed by three radiologists and a hip arthroscopy specialist for labral and cartilage abnormalities. Sensitivity, specificity, and accuracy were calculated using arthroscopy as reference standard. Statistical comparisons between CS and CSAI were performed using McNemar's test. Labral abnormality detection showed excellent sensitivity for radiologists (CS and CSAI: 97-100%) and the surgeon (CS: 81%, CSAI: 90%, p = 0.08), with 100% specificity. Overall cartilage lesion sensitivity was significantly higher with CSAI versus CS (42% vs. 37%, p < 0.001). Highest sensitivity was observed in superolateral acetabular cartilage (CS: 81%, CSAI: 88%, p < 0.001), while highest specificity was found for the anteroinferior acetabular cartilage (CS and CSAI: 99%). Sensitivity was lowest for the assessment of the anteroinferior and posterior acetabular zones, and inferior and posterior femoral zones (CS and CSAI < 6%). CS and CSAI MR imaging showed excellent diagnostic performance for labral abnormalities. Despite CSAI's improved cartilage lesion detection, overall diagnostic performance for cartilage assessment remained suboptimal. Question Accurate preoperative detection of labral and cartilage lesions in femoroacetabular impingement remains challenging, with current MRI protocols showing variable diagnostic performance. Findings High-resolution deep learning-based and standard-resolution compressed sense MRI demonstrate comparable diagnostic performance, with high accuracy for labral defects but limited sensitivity for cartilage lesions. Clinical relevance Current MRI protocols, regardless of resolution optimization, show persistent limitations in cartilage evaluation, indicating the need for further technical advancement to improve diagnostic confidence in presurgical planning.