Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning.
Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning.
复制标题
膝关节软骨厚度随年龄变化：通过深度学习对 2,481 名受试者进行 3-T 磁共振研究
DOI：
10.3389/fmed.2020.600049
复制
发表时间：
2020
影响因子：
3.9
通讯作者：
Yao W
中科院分区：
文献类型：
--
作者：
Si L;Xuan K;Zhong J;Huo J;Xing Y;Geng J;Hu Y;Zhang H;Wang Q;Yao W
背景：在一般人群中，很难区分整个膝关节的软骨变薄，也很难追踪软骨形态随年龄的变化，这对于骨关节炎的研究具有重要意义，直到大的影像学数据和人工智能融合起来。我们研究的目的是：(1)在大量健康膝关节中探索膝关节解剖区域的软骨厚度，(2)研究软骨变薄模式与年龄增长之间的关系。方法：回顾性研究2481例健康膝关节患者，年龄15 ~ 64岁，平均年龄35±10岁。利用3-T超导扫描仪获取的膝关节磁共振图像，通过深度学习自动精确分割软骨，并计算出14个解剖区域的软骨厚度。通过考虑年龄、性别和侧面因素，使用方差分析比较厚度读数。通过回归分析进一步追踪软骨厚度变薄模式与年龄增长的关系。结果：股骨软骨厚度均大于胫骨软骨厚度（p < 0.05）。回归分析显示，除胫骨前内侧和外侧外，男女各区域软骨均随年龄增长而变薄（p < 0.05）。男性股骨前内侧和前外侧变薄速度快于女性，髌骨内侧变薄速度慢于女性（p < 0.05）。结论：我们利用大数据和深度学习建立了软骨厚度的计算方法。我们证明了膝关节各个区域的软骨厚度不同。软骨随着年龄的增长而变薄，并且在胫骨中变薄的模式是一致的，而在两性之间的髌骨和股骨中则不一致。这些结果为检测软骨异常提供了潜在的参考。
Background: It was difficult to distinguish the cartilage thinning of an entire knee joint and to track the evolution of cartilage morphology alongside ages in the general population, which was of great significance for studying osteoarthritis until big imaging data and artificial intelligence are fused. The purposes of our study are (1) to explore the cartilage thickness in anatomical regions of the knee joint among a large collection of healthy knees, and (2) to investigate the relationship between the thinning pattern of the cartilages and the increasing ages. Methods: In this retrospective study, 2,481 healthy knees (subjects ranging from 15 to 64 years old, mean age: 35 ± 10 years) were recruited. With magnetic resonance images of knees acquired on a 3-T superconducting scanner, we automatically and precisely segmented the cartilage via deep learning and calculated the cartilage thickness in 14 anatomical regions. The thickness readings were compared using ANOVA by considering the factors of age, sex, and side. We further tracked the relationship between the thinning pattern of the cartilage thickness and the increasing ages by regression analysis. Results: The cartilage thickness was always thicker in the femur than corresponding regions in the tibia (p < 0.05). Regression analysis suggested cartilage thinning alongside ages in all regions (p < 0.05) except for medial and lateral anterior tibia in both females and males (p > 0.05). The thinning speed of men was faster than women in medial anterior and lateral anterior femur, yet slower in the medial patella (p < 0.05). Conclusion: We established the calculation method of cartilage thickness using big data and deep learning. We demonstrated that cartilage thickness differed across individual regions in the knee joint. Cartilage thinning alongside ages was identified, and the thinning pattern was consistent in the tibia while inconsistent in patellar and femoral between sexes. These findings provide a potential reference to detect cartilage anomaly.