Knee Cartilage Thickness Differs Alongside Ages: A 3-T Magnetic Resonance Research Upon 2,481 Subjects via Deep Learning

被引:18
作者
Si, Liping [1 ]
Xuan, Kai [2 ]
Zhong, Jingyu [1 ]
Huo, Jiayu [2 ]
Xing, Yue [1 ]
Geng, Jia [3 ]
Hu, Yangfan [4 ]
Zhang, Huan [5 ]
Wang, Qian [2 ]
Yao, Weiwu [1 ]
机构
[1] Shanghai Jiao Tong Univ, Tongren Hosp, Dept Imaging, Sch Med, Shanghai, Peoples R China
[2] Shanghai Jiao Tong Univ, Sch Biomed Engn, Inst Med Imaging Technol, Shanghai, Peoples R China
[3] Zhejiang Univ, Dept Radiol, Affiliated Hosp 1, Coll Med, Hangzhou, Peoples R China
[4] Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6, Dept Radiol, Shanghai, Peoples R China
[5] Shanghai Jiao Tong Univ, Dept Radiol, Sch Med, Ruijin Hosp, Shanghai, Peoples R China
基金
中国国家自然科学基金;
关键词
knee; cartilage; deep learning; MR; thickness; OSTEOARTHRITIS; EPIDEMIOLOGY; PREVALENCE; DISEASE; BURDEN; HIP;
D O I
10.3389/fmed.2020.600049
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
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.
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页数:10
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