Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: The predicted damage area (mean 0.021 mm(2)) of the model optimized by deep learning was consistent with the experimental results (mean 0.023 mm(2)). 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. CONCLUSION: It is feasible to use CNN to optimize the manufacturing of the aortic arch models.