Three-dimensional deep neural network integrating transfer learning for preoperative coronary CTA classification in atrial fibrillation patients
10.3969/j.issn.1005-202X.2025.09.017
- VernacularTitle:三维深度网络结合迁移学习的房颤患者冠脉CTA术前分类
- Author:
Wei CHEN
1
;
Zirui XIN
;
Xi CHEN
;
Zhenjiang LIU
;
Aijing LUO
Author Information
1. 中南大学湘雅二医院,湖南 长沙 410011;中南大学生命科学学院,湖南 长沙 410013;医学信息研究湖南省普通高等学校重点实验室(中南大学),湖南 长沙 410013;湖南省心血管智能医疗临床医学研究中心,湖南 长沙 410011
- Publication Type:Journal Article
- Keywords:
atrial fibrillation;
coronary computed tomography angiography;
three-dimensional deep neural network;
transfer learning;
catheter ablation;
surgical strategy
- From:
Chinese Journal of Medical Physics
2025;42(9):1245-1254
- CountryChina
- Language:Chinese
-
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.
