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

项目总结 青少年特发性脊柱侧凸(AIS)影响2-4%的青少年人口。人工智能导致了一个三维 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 如果不治疗，就会出现功能障碍、早发性骨关节炎和椎间盘退变。进展性脊柱融合术 脊柱侧弯的病例仍然是主要的治疗选择。患者的术前特征的变化， 外科植入物，手术操作已经产生了广泛的手术结果，其中20% 仍然不尽如人意。由于次优的手术结果可能会显著影响成本、风险 青少年患者的翻修手术和长期康复，客观的患者特有的模式， 可以预测不同手术方案的结果，并确定最佳的手术干预 对于个人来说是迫切需要的。这项拟议工作的中心假设是，确定关键 术前脊柱三维曲度特征及术中手术干预对手术效果的影响 长期结果可以为预测该患者的手术结果提供一个量化的框架。 人口。为此，我们建议(I)确定患者特定的和外科医生可修改的预测因子 使用机器学习的外科AIS患者的内部数据库中的脊柱融合结果，(Ii)开发一种 手术前病人状况和手术方式对预后的概率预测模型 干预措施和(3)开发一个完全自动化的框架，允许在线图像处理和分配 基于先验知识以概率方式决定新患者手术结果的治疗选项 算法。该方法的创新之处在于开发了第一个数据驱动的外科手术预测模型 AIS患者的规划，允许通过概率预测比较不同的治疗方案 框架和建议的手术干预，从而为给定的患者带来最佳的结果。这 基于知识的算法从医学图像中自动提取脊柱曲线模式作为 分类器。开发一种自动图像处理算法来开发降阶模型 脊柱畸形可以进行快速的定量分析，适合于直接临床传播。它的目的是 该模型可作为临床AIS患者个性化手术决策的辅助工具 设置。这一辅助工具将使用一个大型的医学图像数据库进行培训和测试 AIS患者，可以通过开发一种量化方法来考虑到 A手术方法的组合，并提供改善脊柱预后的建议 儿科人群中的畸形手术。

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.