Van Haver A, Mahieu P, Claessens T, Li H, Pattyn C, Verdonk P, Audenaert E A
Department of Mechanics, BioMech, University College Ghent, Valentin Vaerwijckweg 1, 9000 Ghent, Belgium; Department of Production and Construction, Ghent University, 9052 Zwijnaarde, Belgium; Monica Orthopaedic Research Institute (MORE Institute), 2100 Antwerp, Belgium.
Department of Physical medicine and Orthopaedic Surgery, Ghent University, De Pintelaan 185, 9000 Ghent, Belgium.
Knee. 2014 Mar;21(2):518-23. doi: 10.1016/j.knee.2013.11.016. Epub 2013 Dec 1.
Trochlear dysplasia is known as the primary predisposing factor for patellar dislocation. Current methods to describe trochlear dysplasia are mainly qualitative or based on a limited number of discrete measurements. The purpose of this study is to apply statistical shape analysis to take the full geometrical complexity of trochlear dysplasia into account.
Statistical shape analysis was applied to 20 normal and 20 trochlear dysplastic distal femur models, including the cartilage.
This study showed that the trochlea was anteriorized, proximalized and lateralized and that the mediolateral width and the notch width were decreased in the trochlear dysplastic femur compared to the normal femur. The first three principal components of the trochlear dysplastic femurs, accounting for 79.7% of the total variation, were size, sulcus angle and notch width. Automated classification of the trochlear dysplastic and normal femora achieved a sensitivity of 85% and a specificity of 95%.
This study shows that shape analysis is an outstanding method to visualise the location and magnitude of shape abnormalities. Improvement of automated classification and subtyping within the trochlear dysplastic group are expected when larger training sets are used.
Classification of trochlear dysplasia, especially borderline cases may be facilitated by automated classification. Furthermore, the identification of a decreased notch width in association with an increased sulcus angle can also contribute to the diagnosis of trochlear dysplasia.
滑车发育不良被认为是髌骨脱位的主要诱发因素。目前描述滑车发育不良的方法主要是定性的或基于有限数量的离散测量。本研究的目的是应用统计形状分析来全面考虑滑车发育不良的几何复杂性。
对20个正常和20个滑车发育不良的股骨远端模型（包括软骨）应用统计形状分析。
本研究表明，与正常股骨相比，滑车发育不良的股骨中，滑车向前、近端和外侧移位，并且内外侧宽度和切迹宽度减小。滑车发育不良股骨的前三个主成分，占总变异的79.7%，分别是大小、沟角和切迹宽度。滑车发育不良和正常股骨的自动分类灵敏度为85%，特异性为95%。
本研究表明，形状分析是一种可视化形状异常位置和程度的优秀方法。当使用更大的训练集时，预计滑车发育不良组内的自动分类和亚型划分会得到改善。
自动分类可能有助于滑车发育不良的分类，尤其是临界病例。此外，切迹宽度减小与沟角增加的识别也有助于滑车发育不良的诊断。
