【摘要】 目的：基于三维深度网络构建房颤患者冠脉CTA术前分类模型，探讨迁移学习技术对医学影像分类模型性能的影响，为导管消融提供术前决策支持，推动房颤治疗向精确化、个性化转变。方法：分别以3D ConvNet和3D-ResNet作为骨干网络，提取冠脉CTA序列的三维分类特征；利用公开预训练权重进行迁移学习，并通过混淆矩阵、准确率、AUC等指标评估模型分类效果，对比分析迁移学习模型与初始化训练模型之间的性能差异。结果：迁移学习显著改善了模型性能，与初始化训练模型相比，各个迁移学习模型的分类AUC提升9.1%～16.7%，准确率提高6.2%～23.5%，其中结合MedicalNet预训练权重的3D-ResNet18模型表现最佳，AUC达0.77，准确率为0.71。结论：结合迁移学习的三维深度网络能通过术前冠脉CTA有效识别需在肺静脉隔离基础上附加额外消融的房颤患者，这将有助于引导临床医生优化手术策略并改善治疗效果，从而降低术后远期复发率。更多还原

【Abstract】 Objective To develop a three-dimensional(3D) deep neural network based preoperative classification model for coronary computed tomography angiography(CTA) in atrial fibrillation patients, and to explore the effects of transfer learning on the performance of medical image classification models, thereby providing preoperative decision support for catheter ablation to advance atrial fibrillation treatment toward precision and personalization. Methods Utilizing 3D ConvNet and 3D ResNet as backbone network, the three-dimensional classification features were extracted from coronary CTA sequences. The publicly available pre-trained weights were used for transfer learning. The model performance was evaluated through metrics such as confusion matrix, classification accuracy, and area under the curve(AUC). A comparative analysis was also conducted to evaluate the performance differences between the transfer learning model and the initialized training model. Results Transfer learning yielded significant performance improvements over the initialized training models, attaining AUC improvement of 9.1%-16.7%and accuracy enhancement of 6.2%-23.5%. Among all models, 3D-ResNet18 model with MedicalNet pre-training weights performed the best, achieving an AUC of 0.77 and an accuracy of 0.71. Conclusion The proposed three-dimensional deep network enhanced by transfer learning can effectively identify atrial fibrillation patients requiring additional ablation besides pulmonary vein isolation through preoperative coronary CTA, which will assist clinicians in optimizing surgical strategies and improving treatment outcomes, thereby reducing long-term postoperative recurrence rates.更多还原