膝关节软骨厚度的变化。一种新型机器学习算法在磁共振图像软骨分割中的应用。
Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images.
机构信息
Department of Orthopedic Surgery, University of California-San Francisco, San Francisco, CA.
Department of Radiology, University of California-San Francisco, Musculoskeletal and Imaging Research Group, San Francisco, CA.
出版信息
J Arthroplasty. 2019 Oct;34(10):2210-2215. doi: 10.1016/j.arth.2019.07.022. Epub 2019 Jul 24.
BACKGROUND
The variation in articular cartilage thickness (ACT) in healthy knees is difficult to quantify and therefore poorly documented. Our aims are to (1) define how machine learning (ML) algorithms can automate the segmentation and measurement of ACT on magnetic resonance imaging (MRI) (2) use ML to provide reference data on ACT in healthy knees, and (3) identify whether demographic variables impact these results.
METHODS
Patients recruited into the Osteoarthritis Initiative with a radiographic Kellgren-Lawrence grade of 0 or 1 with 3D double-echo steady-state MRIs were included and their gender, age, and body mass index were collected. Using a validated ML algorithm, 2 orthogonal points on each femoral condyle were identified (distal and posterior) and ACT was measured on each MRI. Site-specific ACT was compared using paired t-tests, and multivariate regression was used to investigate the risk-adjusted effect of each demographic variable on ACT.
RESULTS
A total of 3910 MRI were included. The average femoral ACT was 2.34 mm (standard deviation, 0.71; 95% confidence interval, 0.95-3.73). In multivariate analysis, distal-medial (-0.17 mm) and distal-lateral cartilage (-0.32 mm) were found to be thinner than posterior-lateral cartilage, while posterior-medial cartilage was found to be thicker (0.21 mm). In addition, female sex was found to negatively impact cartilage thickness (OR, -0.36; all values: P < .001).
CONCLUSION
ML was effectively used to automate the segmentation and measurement of cartilage thickness on a large number of MRIs of healthy knees to provide normative data on the variation in ACT in this population. We further report patient variables that can influence ACT. Further validation will determine whether this technique represents a powerful new tool for tracking the impact of medical intervention on the progression of articular cartilage degeneration.
背景
健康膝关节的关节软骨厚度（ACT）变化难以量化，因此记录不佳。我们的目标是：(1) 定义机器学习 (ML) 算法如何自动分割和测量磁共振成像 (MRI) 上的 ACT；(2) 使用 ML 提供健康膝关节 ACT 的参考数据；(3) 确定人口统计学变量是否会影响这些结果。
方法
本研究纳入了在 Osteoarthritis Initiative 中招募的影像学 Kellgren-Lawrence 分级为 0 或 1 级且具有 3D 双回波稳态 MRI 的患者，收集了他们的性别、年龄和体重指数。使用经过验证的 ML 算法，在每个股骨髁上识别出 2 个正交点（远端和后部），并在每个 MRI 上测量 ACT。使用配对 t 检验比较特定部位的 ACT，使用多变量回归分析调查每个人口统计学变量对 ACT 的风险调整效应。
结果
共纳入 3910 个 MRI。平均股骨 ACT 为 2.34 毫米（标准差为 0.71；95%置信区间为 0.95-3.73）。在多变量分析中，发现内侧远端 (-0.17 毫米) 和外侧远端 (-0.32 毫米) 软骨比外侧后软骨薄，而内侧后软骨较厚 (0.21 毫米)。此外，发现女性性别会对软骨厚度产生负面影响（OR，-0.36；所有值：P<.001）。
结论
ML 有效地用于自动分割和测量大量健康膝关节 MRI 上的软骨厚度，为该人群 ACT 变化提供了规范数据。我们进一步报告了可能影响 ACT 的患者变量。进一步的验证将确定该技术是否代表了一种强大的新工具，用于跟踪医疗干预对关节软骨退变进展的影响。
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