Philips Research North America, 345 Scarborough Rd, Briarcliff Manor, NY 10510, USA.
Eur Spine J. 2012 Jan;21(1):40-9. doi: 10.1007/s00586-011-2004-2. Epub 2011 Aug 31.
Understanding how to classify and quantify three-dimensional (3D) spinal deformities remains an open question in adolescent idiopathic scoliosis. The objective of this study was to perform a 3D manifold characterization of scoliotic spines demonstrating thoracic deformations using a novel geometric and intuitive statistical tool to determine patterns in pathological cases.
Personalized 3D reconstructions of thoracic (T)/lumbar (L) spines from a cohort of 170 Lenke Type-1 patients were analyzed with a non-linear manifold embedding algorithm in order to reduce the high-dimensionality of the data, using statistical properties of neighbouring spine models. We extracted sub-groups of the data from the underlying manifold structure using an unsupervised clustering algorithm to understand the inherent distribution and determine classes of pathologies which appear from the low-dimensional space.
For Lenke Type-1 patients, four clusters were detected from the low-dimensional manifold of 3D models: (1) normal kyphosis (T) with hyper-lordosis (L) and high Cobb angles (37 cases), (2) low kyphosis (T) and normal lordosis (L), with high rotation of plane of maximum curvature (55 cases), (3) hypo-kyphotic (T) and hyper-lordosis (L) (21 cases) and (4) hyper-kyphotic (T) with strong vertebral rotation (57 cases). Results show the manifold representation can potentially be useful for classification of 3D spinal pathologies such as idiopathic scoliosis and serve as a tool for understanding the progression of deformities in longitudinal studies.
Quantitative evaluation illustrates that the complex space of spine variability can be modeled by a low-dimensional manifold and shows the existence of an additional hyper-kyphotic subgroup from the cohort of 3D spine reconstructions of Lenke Type-1 patients when compared with previous findings on the 3D classification of spinal deformities.
在青少年特发性脊柱侧凸中，如何对三维（3D）脊柱畸形进行分类和量化仍是一个悬而未决的问题。本研究的目的是使用新颖的几何和直观的统计工具，对表现出胸段畸形的脊柱侧凸患者进行 3D 流形特征描述，以确定病理性病例中的模式。
使用非线性流形嵌入算法分析了来自 170 例 Lenke 1 型患者队列的个性化 T/L 脊柱 3D 重建，以降低数据的高维性，同时利用相邻脊柱模型的统计特性。我们使用无监督聚类算法从基础流形结构中提取数据的子组，以了解固有分布并确定从低维空间出现的病理学类别。
对于 Lenke 1 型患者，从 3D 模型的低维流形中检测到四个聚类：（1）正常后凸（T）伴过度前凸（L）和高 Cobb 角（37 例）；（2）低后凸（T）和正常前凸（L），伴最大曲率平面高旋转（55 例）；（3）胸椎后凸不足（T）伴过度前凸（L）（21 例）和（4）胸椎过度前凸（T）伴强烈的椎体旋转（57 例）。结果表明，流形表示法可用于 3D 脊柱病变（如特发性脊柱侧凸）的分类，并且可以作为理解纵向研究中畸形进展的工具。
定量评估表明，脊柱变异性的复杂空间可以通过低维流形进行建模，并且与之前关于脊柱畸形 3D 分类的研究相比，在 Lenke 1 型患者的 3D 脊柱重建队列中存在一个额外的过度前凸亚组。
