PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
摘要：
■髋关节发育不良被认为是导致髋关节退化和最终需要全髋关节置换术(THA)的主要病因之一。我们验证了一种深度学习（DL）算法来测量与髋关节发育不良相关的角度，并应用该算法来确定基于增量射线照相截止值的大量人群中发育不良的患病率。
■纳入有前后骨盆X线片且以前没有THA的骨关节炎患者。DL算法自动化了与髋关节发育不良相关的3个角度：修正的外侧中心边缘角度（LCEA），Tönnis的角度,并修改了锐角。该算法已针对手动测量进行了验证，并且在3869名患者的队列中测量了所有角度（61.2±9.2岁，57.1%女性）。使用每个角度分析了髋关节发育不良的百分位分布和患病率。
■该算法对读者的测量值（配对差异：0.3°-0.7°）没有显着差异（P>.05），并且对发育异常分类具有极好的一致性（κ=0.78-0.88）。140分钟内,对3869名患者进行了23,214次自动化测量。女性的LCEA和Sharp角较高，Tönis角较低（P<.01）。髋关节发育不良的患病率从2.5％到20％，使用以下截止值：17.3°-25.5°（LCEA），9.4°-15.6°(Tönis),和41.3°-45.9°（锐利）。
■开发了一种DL算法来测量和分类轻度髋关节发育不良的髋关节。在一个大型患者队列中，所报告的发育异常的患病率取决于测量值和阈值。12.4%的患者患有发育不良的影像学指标表明THA风险较高。
■纳入有前后骨盆X线片且以前没有THA的骨关节炎患者。DL算法自动化了与髋关节发育不良相关的3个角度：修正的外侧中心边缘角度（LCEA），Tönnis的角度,并修改了锐角。该算法已针对手动测量进行了验证，并且在3869名患者的队列中测量了所有角度（61.2±9.2岁，57.1%女性）。使用每个角度分析了髋关节发育不良的百分位分布和患病率。
■该算法对读者的测量值（配对差异：0.3°-0.7°）没有显着差异（P>.05），并且对发育异常分类具有极好的一致性（κ=0.78-0.88）。140分钟内,对3869名患者进行了23,214次自动化测量。女性的LCEA和Sharp角较高，Tönis角较低（P<.01）。髋关节发育不良的患病率从2.5％到20％，使用以下截止值：17.3°-25.5°（LCEA），9.4°-15.6°(Tönis),和41.3°-45.9°（锐利）。
■开发了一种DL算法来测量和分类轻度髋关节发育不良的髋关节。在一个大型患者队列中，所报告的发育异常的患病率取决于测量值和阈值。12.4%的患者患有发育不良的影像学指标表明THA风险较高。
