Northwestern University McCormick School of Engineering, Evanston, IL, USA.
Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA.
Stud Health Technol Inform. 2021 Jun 28;280:141-145. doi: 10.3233/SHTI210453.
Scoliosis is a 3D deformation of the spinal column, characterized by a lateral deviation of the spine, accompanied by axial rotation of the vertebrae. Adolescent Idiopathic Scoliosis (AIS), is the most common type, affecting children between ages 8 to 18 when bone growth is at its maximum rate. The selection of the most appropriate treatment options is based on the surgeon's experience. So, developing a clinically validated patient-specific model of the spine would aid surgeons in understanding AIS in early stages and propose an efficient method of treatment for the individual patient. This project steps include: Developing a clinically validated patient-specific Reduced Order Finite Element Model (ROFEM) of the spine, predicting AIS progression using data mining and proposing a method of treatment. First we implement FE synergistically with bio-mechanical information, image processing and data science techniques to improve predictive ability. Initial geometry of the spine will be extracted from the x-ray images from different planes and imported to FEM software to generate the spine model and perform analysis. A RO model is developed based on the detailed spinal FEM. Next, a neural network is used to predict the spinal curvature. The ability to predict the severity of AIS will have an immense impact on the treatment of AIS-affected children. Access to a predictive and patient-specific model will enable the physicians to have a better understanding of spinal curvature progression. Consequently, the physicians will be able to educate families, choose the most appropriate treatment option and asses for surgical intervention.
脊柱侧凸是脊柱的三维变形，其特征是脊柱的侧向偏离，并伴有椎体的轴向旋转。青少年特发性脊柱侧凸（AIS）是最常见的类型，影响 8 至 18 岁骨骼生长最快的儿童。最合适的治疗方案的选择基于外科医生的经验。因此，开发一种临床验证的患者特异性脊柱简化有限元模型（ROFEM）将有助于外科医生在早期阶段了解 AIS，并为个体患者提出有效的治疗方法。该项目步骤包括：开发一种临床验证的患者特异性脊柱简化有限元模型（ROFEM），使用数据挖掘预测 AIS 进展，并提出一种治疗方法。首先，我们通过生物力学信息、图像处理和数据科学技术协同作用，提高预测能力。初始脊柱几何形状将从不同平面的 X 射线图像中提取出来，并导入有限元软件中生成脊柱模型并进行分析。基于详细的脊柱有限元模型开发了一个简化模型。接下来，使用神经网络来预测脊柱曲率。预测 AIS 严重程度的能力将对 AIS 患儿的治疗产生巨大影响。获得预测性和患者特异性模型将使医生更好地了解脊柱曲率的进展。因此，医生将能够教育家庭，选择最合适的治疗方案，并评估手术干预的必要性。
