Department of Cardiovascular Surgery, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Anatomy, Anhui Medical University, Hefei, China.
Comput Math Methods Med. 2022 Mar 17;2022:6499461. doi: 10.1155/2022/6499461. eCollection 2022.
Preservation of autologous brachiocephalic vessels in Stanford type A aortic dissection has good short-time outcomes. However, getting access to the details is not easy by conventional examination methods. This study is aimed at reconstructing the aortic arch model by three-dimensional (3D) printing based on convolutional neural networks (CNN) to understand the details for performing surgery.
Three patients with type A aortic dissection from October 2017 to June 2018 were indicated for simplified Sun's procedure. Convolutional neural network (CNN) is used as a deep learning model, and the model was preset by transfer learning. The genetic algorithm (GA) was used to optimize the parameters. The aortic arch models were reconstructed using the segmented image.
The predicted damage area (mean 0.021 mm) of the model optimized by deep learning was consistent with the experimental results (mean 0.023 mm). Among the three patients, one patient died due to multiple organ failure and septic shock on the 11th day after surgery. The other two patients were cured, no reoperation was reported, and their cardiac functions were defined as class I during the 13 and 20 months of follow-up.
It is feasible to use CNN to optimize the manufacturing of the aortic arch models.
在 Stanford 型主动脉夹层中保留自体头臂血管具有良好的短期效果。然而，通过常规检查方法很难获得详细信息。本研究旨在通过卷积神经网络（CNN）构建主动脉弓模型的三维（3D）打印，以了解手术细节。
2017 年 10 月至 2018 年 6 月期间，3 名 Stanford 型主动脉夹层患者符合简化 Sun 手术适应证。卷积神经网络（CNN）用作深度学习模型，并通过转移学习预设模型。使用遗传算法（GA）优化参数。使用分割图像重建主动脉弓模型。
通过深度学习优化的模型预测的损伤面积（平均 0.021mm）与实验结果（平均 0.023mm）一致。在这 3 名患者中，1 名患者因多器官功能衰竭和手术后第 11 天感染性休克而死亡。另外 2 名患者痊愈，随访 13 个月和 20 个月时，心功能均定义为 I 级，未报告再次手术。
使用 CNN 优化主动脉弓模型制造是可行的。
