BackgroundHip‐related pain (HRP) affects young to middle‐aged active adults and impacts physical activity, finances and quality of life. HRP includes conditions like femoroacetabular impingement syndrome and labral tears. Lateral hip muscle dysfunction and atrophy in HRP are more pronounced in advanced hip pathology, with limited evidence in younger populations. While MRI use for assessing hip muscle morphology is increasing, with automated deep‐learning techniques showing promise, studies assessing their accuracy are limited. Therefore, we aimed to compare hip intramuscular fat infiltrate (MFI) and muscle volume, in individuals with and without HRP as well as assess the reliability and accuracy of automated machine‐learning segmentations compared with human‐generated segmentation.MethodsThis cross‐sectional study included sub‐elite/amateur football players (Australian football and soccer) with a greater than 6‐month history of HRP [n = 180, average age 28.32, (standard deviation 5.88) years, 19% female] and a control group of sub‐elite/amateur football players without pain [n = 48, 28.89 (6.22) years, 29% female]. Muscle volume and MFI of gluteus maximus, medius, minimis and tensor fascia latae were assessed using MRI. Associations between muscle volume and group were explored using linear regression models, controlling for body mass index, age, sport and sex. A convolutional neural network (CNN) machine‐learning approach was compared with human‐performed muscle segmentations in a subset of participants (n = 52) using intraclass correlation coefficients and Sorensen–Dice index.ResultsWhen considering adjusted estimates of muscle volume, there were significant differences observed between groups for gluteus medius (adjusted mean difference 23 858 mm3 [95% confidence interval 7563, 40 137]; p = 0.004) and tensor fascia latae (6660 mm3 [2440, 13 075]; p = 0.042). No differences were observed between groups for gluteus maximus (18 265 mm3 [−21 209, 50 782]; p = 0.419) or minimus (3893 mm3 [−2209, 9996]; p = 0.21). The CNN was trained for 30 000 iterations and assessed its accuracy and reliability on an independent testing dataset, achieving high segmentation accuracy (mean Sorenson–Dice index >0.900) and excellent muscle volume and MFI reliability (ICC2,1 > 0.900). The CNN outperformed manual raters, who had slightly lower interrater accuracy (Sorensen–Dice index >0.800) and reliability (ICC2,1 > 0.800).ConclusionsThe increased muscle volumes in the symptomatic group compared with controls could be associated with increased myofibrillar size, sarcoplasmic hypertrophy or both. These changes may facilitate greater muscular efficiency for a given load, enabling the athlete to maintain their normal level of function. In addition, the CNNs for muscle segmentation was more efficient and demonstrated excellent reliability in comparison to manual segmentations.

中文翻译：

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与对照组相比，患有髋部相关疼痛的人的肌肉脂肪和体积差异：一种机器学习方法


背景髋部相关疼痛 (HRP) 影响年轻至中年活跃的成年人，并影响身体活动、财务和生活质量。 HRP 包括股骨髋臼撞击综合征和盂唇撕裂等病症。 HRP 中的外侧髋部肌肉功能障碍和萎缩在晚期髋部病理学中更为明显，但在年轻人群中的证据有限。虽然 MRI 用于评估髋部肌肉形态的用途不断增加，并且自动深度学习技术显示出前景，但评估其准确性的研究仍然有限。因此，我们的目的是比较有和没有 HRP 的个体的髋部肌内脂肪浸润 (MFI) 和肌肉体积，并评估自动机器学习分割与人工生成分割相比的可靠性和准确性。方法这项横断面研究包括具有超过 6 个月 HRP 病史的亚精英/业余足球运动员（澳大利亚橄榄球和足球）[n = 180，平均年龄 28.32，（标准差 5.88）岁，19% 女性]和亚精英/业余足球运动员（19% 女性）无疼痛的精英/业余足球运动员 [n = 48, 28.89 (6.22) 岁，29% 为女性]。使用 MRI 评估臀大肌、臀中肌、臀小肌和阔筋膜张肌的肌肉体积和 MFI。使用线性回归模型探索肌肉体积和群体之间的关联，并控制体重指数、年龄、运动和性别。使用类内相关系数和 Sorensen-Dice 指数，将卷积神经网络 (CNN) 机器学习方法与人类在参与者子集 (n = 52) 中执行的肌肉分割进行比较。结果当考虑调整肌肉体积估计值时，在臀中肌（调整后平均差 23 858 mm3 [95% 置信区间 7563, 40 137]；p = 0.004）和阔筋膜张肌（6660 mm3 [2440， 13 075]；p = 0.042）。臀大肌（18 265 mm3 [−21 209, 50 782]；p = 0.419）或臀小肌（3893 mm3 [−2209, 9996]；p = 0.21）组之间没有观察到差异。 CNN 经过 30 000 次迭代训练，并在独立测试数据集上评估其准确性和可靠性，实现了高分割精度（平均 Sorenson-Dice 指数 >0.900）以及出色的肌肉体积和 MFI 可靠性（ICC2,1 > 0.900） 。 CNN 的表现优于人工评分者，后者的评分者间准确度（Sorensen-Dice 指数 >0.800）和可靠性（ICC2,1 > 0.800）稍低。 结论 与对照组相比，有症状组的肌肉体积增加可能与肌原纤维增加有关大小、肌浆肥大或两者兼而有之。这些变化可能有助于在给定负荷下提高肌肉效率，使运动员能够保持正常的功能水平。此外，与手动分割相比，用于肌肉分割的 CNN 更高效，并且表现出出色的可靠性。