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Periacetabular osteotomy (PAO) is a common treatment for prearthritic hip dysplasia. The goal of this investigation was to determine if computationally assessed hip contact mechanics are associated with joint failure at minimum 10‐year follow‐up. One hundred patients with hip dysplasia (125 hips) completed patient‐reported outcomes an average of 13.8 years (range 10.0–18.0 years) after PAO. 63/125 hips were classified as having failed: 26 converted to total hip arthroplasty (THA) and 37 with significant disability indicated by modified Harris Hip Score (mHHS) ≤ 70. Differences in discrete element analysis‐computed contact mechanics were compared between (1) preserved and failed hips, (2) preserved hips and hips that failed by THA, and (3) preserved hips and hips that failed by mHHS ≤ 70. Failed hips had significantly higher preoperative contact stress and exposure metrics (p < 0.001–0.009) than preserved hips. Failed hips also had significantly higher postoperative peak contact stress (p = 0.018), higher mean contact stress (p < 0.001), and smaller contact area (p = 0.044). When assessed based on type of failure, hips that failed by THA had significantly higher postoperative contact stress and exposure metrics than preserved hips (p < 0.001–0.020). In hips that failed by mHHS ≤ 70, mean postoperative contact stress exposure was significantly higher compared to preserved hips (p = 0.043). Despite improved radiographic measures of dysplasia after PAO, pathologic joint contact mechanics can persist and predict treatment failure at minimum 10 years after surgery. Operative and nonoperative techniques specifically intended to reduce harmful contact mechanics in dysplastic hips may have the potential to further improve clinical outcomes after PAO.
Periacetabular osteotomy (PAO) is a common treatment for prearthritic hip dysplasia. The goal of this investigation was to determine if computationally assessed hip contact mechanics are associated with joint failure at minimum 10‐year follow‐up. One hundred patients with hip dysplasia (125 hips) completed patient‐reported outcomes an average of 13.8 years (range 10.0–18.0 years) after PAO. 63/125 hips were classified as having failed: 26 converted to total hip arthroplasty (THA) and 37 with significant disability indicated by modified Harris Hip Score (mHHS) ≤ 70. Differences in discrete element analysis‐computed contact mechanics were compared between (1) preserved and failed hips, (2) preserved hips and hips that failed by THA, and (3) preserved hips and hips that failed by mHHS ≤ 70. Failed hips had significantly higher preoperative contact stress and exposure metrics (p < 0.001–0.009) than preserved hips. Failed hips also had significantly higher postoperative peak contact stress (p = 0.018), higher mean contact stress (p < 0.001), and smaller contact area (p = 0.044). When assessed based on type of failure, hips that failed by THA had significantly higher postoperative contact stress and exposure metrics than preserved hips (p < 0.001–0.020). In hips that failed by mHHS ≤ 70, mean postoperative contact stress exposure was significantly higher compared to preserved hips (p = 0.043). Despite improved radiographic measures of dysplasia after PAO, pathologic joint contact mechanics can persist and predict treatment failure at minimum 10 years after surgery. Operative and nonoperative techniques specifically intended to reduce harmful contact mechanics in dysplastic hips may have the potential to further improve clinical outcomes after PAO.
The objective was to use convolutional neural networks (CNNs) for automatic segmentation of hip cartilage and labrum based on 3D MRI. In this retrospective single-center study, CNNs were used to develop a fully automated segmentation model for hip cartilage and labrum from MRI. Direct hip MR arthrographies (01/2020-10/2021) were selected from 100 symptomatic patients. Institutional routine protocol included a 3D T1 mapping sequence, which was used for manual segmentation of hip cartilage and labrum. 80 hips were used for training and the remaining 20 for testing. Model performance was assessed with six evaluation metrics including Dice similarity coefficient (DSC). In addition, model performance was tested on an external dataset (40 patients) with a 3D T2-weighted sequence from a different institution. Inter-rater agreement of manual segmentation served as benchmark for automatic segmentation performance. 100 patients were included (mean age 30 ± 10 years, 64% female patients). Mean DSC for cartilage was 0.92 ± 0.02 (95% confidence interval [CI] 0.92–0.93) and 0.83 ± 0.04 (0.81–0.85) for labrum and comparable (p = 0.232 and 0.297, respectively) to inter-rater agreement of manual segmentation: DSC cartilage 0.93 ± 0.04 (0.92–0.95); DSC labrum 0.82 ± 0.05 (0.80–0.85). When tested on the external dataset, the DSC was 0.89 ± 0.02 (0.88–0.90) and 0.71 ± 0.04 (0.69–0.73) for cartilage and labrum, respectively. The presented deep learning approach accurately segments hip cartilage and labrum from 3D MRI sequences and can potentially be used in clinical practice to provide rapid and accurate 3D MRI models.
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