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.
■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。