Data-Driven Framework for Classification and Surgical Planning of Spinal Deformity.

项目摘要 青少年特发性脊柱侧凸（AIS）影响2-4%的青少年人口。AIS导致了一个三维的 脊柱畸形，影响患者的正常运动和姿势，并可能导致肺和心脏 功能障碍、早发性骨关节炎和椎间盘退变（如果不治疗）。渐进性脊柱融合术 例脊柱侧凸仍然是主要的治疗选择。患者术前特征的变化， 手术植入物和手术操作导致了广泛的手术结果，其中20% 仍然不太令人满意。由于次优手术结局可能会显著影响成本、风险 翻修手术和青少年患者的长期康复，客观的患者特定模型， 可以预测不同手术治疗方案的结果并确定最佳手术干预 对于个人来说，这是非常必要的。这项工作的中心假设是， 术前3D脊柱曲线的特征和术中手术干预对 长期结果可以为预测该患者的手术结果提供定量框架 人口为此，我们建议（i）确定患者特异性和外科医生可修改的预测因子， 使用机器学习在手术AIS患者的内部数据库中进行脊柱融合结果，（ii）开发一个 结果的概率预测模型作为术前患者状况和手术时间的函数 （三）开发一个完全自动化的框架，允许在线图像处理，并分配一个 治疗选项，其基于先前的学习概率性地确定新患者的手术结果 算法这种方法的创新之处在于开发了第一个数据驱动的手术预测模型， AIS患者的计划，允许通过概率预测比较不同的治疗方案 框架和推荐手术干预，导致给定患者的最佳结果。这 基于知识的算法从医学图像中自动提取脊柱曲线图案作为 分类器利用一种自动图像处理算法来开发一个简化的有序模型， 脊柱畸形允许适合于直接临床传播的快速定量分析。它瞄准成 将该模型作为临床AIS患者个性化手术决策的辅助工具， 设置。这个辅助工具，将使用一个大型的医学图像数据库进行训练和测试， AIS患者，可以通过开发一种定量方法， 手术方法的组合，并提供建议，以实现改善的结果，脊柱 儿童畸形手术。

PROJECT SUMMARY Adolescent idiopathic scoliosis (AIS) impacts 2-4% of the adolescent population. AIS causes a three- dimensional deformity of the spinal column affecting the patients’ normal motion and posture and may cause lung and heart dysfunction, early onset osteoarthritis, and disc degeneration if left untreated. Spinal fusion surgery in progressive cases of scoliosis remains the main treatment option. The variation in patients’ pre-operative characteristics, the surgical implants, and the surgical maneuvers have resulted in a wide range of surgical outcomes, 20% of which remains to be less than satisfactory. As the suboptimal surgical outcomes can significantly impact the cost, risk of revision surgery, and long-term rehabilitation of the adolescent patients, objective patient-specific models that can predict the outcome of different surgical treatment scenarios and determine the optimal surgical intervention for individuals are of critical need. The central hypothesis of the proposed work is that identifying the key features of a 3D spinal curve before the operation and the intraoperative surgical interventions the influence the long-term outcomes can provide a quantitative framework for predicting the surgical outcomes in this patient population. To this end, we propose (i) to identify the patient-specific and surgeon modifiable predictors of the spinal fusion outcomes in an in-house database of surgical AIS patients using machine learning, (ii) to develop a probabilistic predictive model of the outcomes as a function of pre-operative patient condition and the surgical interventions and (iii) to develop a fully automated framework that allows online image processing and assigns a treatment option that probabilistically determines the surgical outcome for a new patient based on a prior learning algorithm. The innovation of this approach is in developing the first data-driven predictive model for surgical planning of AIS patients that allows comparing different treatment scenarios through a probabilistic predictive framework and recommending surgical intervention that leads to an optimal outcome for a given patient. This knowledge-based algorithm automatically extracts the spinal curve patterns from the medical images as a classifier. The exploitation of an automated image processing algorithm to develop a reduced ordered model of the spinal deformity allows a fast quantitative analysis appropriate for direct clinical dissemination. It is aimed to use this model as an assistive tool for personalized surgical decision making of the AIS patients in the clinical setups. This assistive tool, which will be trained and tested using a large database of the medical images of the AIS patients, can make significant contribution to the field by developing a quantitative approach that considers a combinations of surgical methods and provides recommendations to achieve an improved outcome of the spinal deformity surgery in the pediatric population.