West China Biomedical Big Data Center, West China Hospital/West China School of Medicine, Sichuan University, Chengdu 610041, China.
School of Mathematics, Sichuan Normal University, Chengdu 610066, China.
J Healthc Eng. 2020 Sep 16;2020:8886599. doi: 10.1155/2020/8886599. eCollection 2020.
The objective of this study was to construct a procedural planning tool to optimize the proximal junction angle (PJA) to prevent postoperative proximal junctional kyphosis (PJK) for each scoliosis patient.
Twelve patients (9 patients without PJK and 3 patients with PJK) who have been followed up for at least 2 years after surgery were included. After calculating the loading force on the cephalad intervertebral disc of upper instrumented vertebra of each patient, the finite-element method (FEM) was performed to calculate the stress of each element. The stress information was summarized into the difference value before and after operation in different regions of interest. A two-layer fully connected neural network method was applied to model the relationship between the stress information and the risk of PJK. Leave-one-out cross-validation and sensitivity analysis were implemented to assess the accuracy and stability of the trained model. The optimal PJA was predicted based on the learned model by optimization algorithm.
The mean prediction accuracy was 83.3% for all these cases, and the area under the curve (AUC) of prediction was 0.889. And the output variance of this model was less than 5% when the important factor values were perturbed in a range of 5%.
Our approach integrated biomechanics and machine learning to support the surgical decision. For a new individual, the risk of PJK and optimal PJA can be simultaneously predicted based on the learned model.
本研究旨在构建一种手术规划工具，以优化近端交界角（PJA），预防每个脊柱侧弯患者术后近端交界后凸（PJK）。
纳入了 12 名患者（9 名无 PJK 患者和 3 名有 PJK 患者），这些患者术后至少随访 2 年。计算了每个患者上位器械椎的头端椎间盘的加载力后，采用有限元法（FEM）计算每个元素的应力。将应力信息总结为不同感兴趣区域手术前后的差值。采用两层全连接神经网络方法来建立应力信息与 PJK 风险之间的关系模型。采用留一法交叉验证和敏感性分析来评估训练模型的准确性和稳定性。基于学习模型，通过优化算法预测最佳 PJA。
所有这些病例的平均预测准确率为 83.3%，预测的曲线下面积（AUC）为 0.889。当在 5%的范围内对重要因素值进行扰动时，该模型的输出方差小于 5%。
我们的方法将生物力学和机器学习相结合，为手术决策提供支持。对于新个体，可以根据学习模型同时预测 PJK 的风险和最佳 PJA。
