Evaluation of scoliosis using baropodometer and artificial neural network

Abstract Introduction: One of the most recurrent pathologies in the spine is scoliosis. It occurs in the frontal plane and is formed by one or more curves in the spinal column. The scoliosis causes global postural misalignment in an individual. One of the modifications produced by postural misalignment is the way in which an individual distributes weight to the feet. We aimed to implement an electronic system for separating patients with Degree I scoliosis (i.e., 1° to 19° scoliosis according to the Ricard classification) into two groups: C1 (1°-9°) and C2 (10°-9°). The highest percentage of patients with scoliosis is in this range: those who do not need to wear vests or undergo surgery and whose treatment is performed via special physical exercise and frequent evaluations by healthcare professionals. Methods The electronic system consists of a baropodometer and artificial neural networks (ANNs). The classification of patients in the scoliosis groups was performed with MATLAB software and a Single Layer Perceptron network using the backpropagation training algorithm. Evaluations were performed on 63 volunteers. Results The mean classification sensitivity was 93.7% in the C1 group and 94.5% in the C2 group. The classification accuracy was 83.3% in the C1 group and 96.0% in the C2 group. Conclusion The implemented system can contribute to the treatment of patients with scoliosis grades ranging from 1° to 19°, which represents the highest incidence of this pathology, for which the monitoring of the clinical condition using noninvasive techniques is of fundamental importance.

摘要与引言：脊柱最为常见的病变之一为脊柱侧凸（scoliosis）。该病变累及冠状面，以脊柱存在一处或多处弯曲畸形为特征。脊柱侧凸可导致患者出现全身性姿势异常，而姿势异常引发的典型改变之一，便是患者足部的负重分布模式发生变化。本研究旨在搭建一套电子系统，用于将Ⅰ度脊柱侧凸（即依据Ricard分级标准，侧凸角度为1°~19°的患者）划分为两组：C1组（1°~9°）与C2组（10°~9°）。该角度区间的脊柱侧凸患者占比最高，此类患者无需穿戴支具或接受手术治疗，仅需通过专项体育锻炼与医疗人员的定期评估即可完成临床干预。
方法：本电子系统由足底压力分析仪（baropodometer）与人工神经网络（artificial neural networks, ANNs）组成。研究借助MATLAB软件，结合采用反向传播训练算法的单层感知器网络，完成脊柱侧凸患者的分组分类任务。本研究共纳入63名志愿者开展评估工作。
结果：C1组的分类平均敏感度达93.7%，C2组为94.5%；C1组的分类准确率为83.3%，C2组为96.0%。
结论：本研究所搭建的电子系统可助力1°~19°脊柱侧凸患者的临床诊疗——该区间为该病变的最高发人群，对此类患者采用无创技术监测临床状态具有核心重要性。