Assi K C, Labelle H, Cheriet F
École Polytechnique de Montréal, P.O. Box 6097, Succursale Centre-ville, Montréal, Québec, Canada H3C 3A7; Sainte-Justine Hospital Research Center, 3175 Côte-Sainte-Catherine, Montréal, Québec, Canada H3T 1C5.
Sainte-Justine Hospital Research Center, 3175 Côte-Sainte-Catherine, Montréal, Québec, Canada H3T 1C5.
Comput Biol Med. 2014 May;48:85-93. doi: 10.1016/j.compbiomed.2014.02.015. Epub 2014 Mar 4.
One of the major concerns of scoliosis patients undergoing surgical treatment is the aesthetic aspect of the surgery outcome. It would be useful to predict the postoperative appearance of the patient trunk in the course of a surgery planning process in order to take into account the expectations of the patient. In this paper, we propose to use least squares support vector regression for the prediction of the postoperative trunk 3D shape after spine surgery for adolescent idiopathic scoliosis. Five dimensionality reduction techniques used in conjunction with the support vector machine are compared. The methods are evaluated in terms of their accuracy, based on the leave-one-out cross-validation performed on a database of 141 cases. The results indicate that the 3D shape predictions using a dimensionality reduction obtained by simultaneous decomposition of the predictors and response variables have the best accuracy.
接受手术治疗的脊柱侧弯患者主要关注的问题之一是手术结果的美观性。在手术规划过程中预测患者术后躯干外观，以便考虑患者的期望，这将是很有用的。在本文中，我们建议使用最小二乘支持向量回归来预测青少年特发性脊柱侧弯脊柱手术后的躯干三维形状。比较了与支持向量机结合使用的五种降维技术。基于对141例病例数据库进行的留一法交叉验证，从准确性方面对这些方法进行了评估。结果表明，使用通过预测变量和响应变量同时分解获得的降维进行三维形状预测具有最佳准确性。
