Jaremko J L, Poncet P, Ronsky J, Harder J, Dansereau J, Labelle H, Zernicke R F
McCaig Centre for Joint Injury & Arthritis Research, Faculty of Medicine, University of Calgary, Alberta, Canada.
Spine (Phila Pa 1976). 2001 Jul 15;26(14):1583-91. doi: 10.1097/00007632-200107150-00017.
Correlation of torso scan and three-dimensional radiographic data in 65 scans of 40 subjects.
To assess whether full-torso surface laser scan images can be effectively used to estimate spinal deformity with the aid of an artificial neural network.
Quantification of torso surface asymmetry may aid diagnosis and monitoring of scoliosis and thereby minimize the use of radiographs. Artificial neural networks are computing tools designed to relate input and output data when the form of the relation is unknown.
A three-dimensional torso scan taken concurrently with a pair of radiographs was used to generate an integrated three-dimensional model of the spine and torso surface. Sixty-five scan-radiograph pairs were generated during 18 months in 40 patients (Cobb angles 0-58 degrees ): 34 patients with adolescent idiopathic scoliosis and six with juvenile scoliosis. Sixteen (25%) were randomly selected for testing and the remainder (n = 49) used to train the artificial neural network. Contours were cut through the torso model at each vertebral level, and the line joining the centroids of area of the torso contours was generated. Lateral deviations and angles of curvature of this line, and the relative rotations of the principal axes of each contour were computed. Artificial neural network estimations of maximal computer Cobb angle were made.
Torso-spine correlations were generally weak (r < 0.5), although the range of torso rotation related moderately well to the maximal Cobb angle (r = 0.64). Deformity of the torso centroid line was minimal despite significant spinal deformity in the patients studied. Despite these limitations and the small data set, the artificial neural network estimated the maximal Cobb angle within 6 degrees in 63% of the test data set and was able to distinguish a Cobb angle greater than 30 degrees with a sensitivity of 1.0 and specificity of 0.75.
Neural-network analysis of full-torso scan imaging shows promise to accurately estimate scoliotic spinal deformity in a variety of patients.
对40名受试者的65次扫描进行躯干扫描与三维放射影像数据的相关性研究。
评估全躯干表面激光扫描图像能否借助人工神经网络有效地用于估计脊柱畸形。
躯干表面不对称的量化有助于脊柱侧弯的诊断和监测，从而减少X光片的使用。人工神经网络是一种计算工具，旨在当关系形式未知时关联输入和输出数据。
将与一对X光片同时进行的三维躯干扫描用于生成脊柱和躯干表面的综合三维模型。在18个月内对40例患者（Cobb角0 - 58度）进行了65次扫描 - 放射影像配对：34例青少年特发性脊柱侧弯患者和6例青少年脊柱侧弯患者。随机选择16例（25%）用于测试，其余（n = 49）用于训练人工神经网络。在每个椎体水平穿过躯干模型切割轮廓，并生成连接躯干轮廓区域质心的线。计算该线的横向偏差和曲率角度，以及每个轮廓主轴的相对旋转。进行人工神经网络对最大计算机Cobb角的估计。
躯干与脊柱的相关性一般较弱（r < 0.5），尽管躯干旋转范围与最大Cobb角的相关性中等良好（r = 0.64）。在所研究的患者中，尽管脊柱存在明显畸形，但躯干质心线的畸形最小。尽管存在这些局限性和数据集较小的情况，人工神经网络在63%的测试数据集中将最大Cobb角估计在6度以内，并且能够以1.0的灵敏度和0.75的特异性区分大于30度的Cobb角。
全躯干扫描成像的神经网络分析显示出有望准确估计各种患者的脊柱侧弯畸形。
