A deep learning model for efficient end-to-end stratification of thrombotic risk in left atrial appendage

• Published in: Engineering applications of artificial intelligence 2023-11, Vol.126, p.107187, Article 107187
• Main Authors: Gao, Qi, Lin, Hongtao, Qian, Jianghong, Liu, Xingli, Cai, Shengze, Li, He, Fan, Hongguang, Zheng, Zhe
• Format: Article
• Language: English
• Subjects: Atrial fibrillation; Computational fluid mechanics; Deep learning; Left atrial appendage thrombosis; PointNet; U-NET
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.107187

Clot formation in the left atrial appendage (LAA) poses a high risk of ischemic strokes and systemic embolism to patients with atrial fibrillation (AF), the most common type of sustained heart arrhythmia that affects more than 35 million people worldwide. Hemodynamic metrics evaluated using computational fluid dynamics (CFD) have been employed to assess the risk of thrombosis in LAA, but its utilization in clinical settings is limited due to their cumbersome operations and high computational cost. To this end, we propose UDGCNN (U: U-net, DGCNN: Dynamic Graph Convolutional Neural Network) that can utilize data from the point cloud of patient-specific LAA geometries as inputs and predict multiple hemodynamic indexes for systematic assessment of the thrombotic risk. The novelty of the proposed model lies in introducing edge convolution layers to the PointNet structure to improve the model’s capacity to learn local features, which is essential for training and predicting complex patient-specific geometries. Moreover, the network structure of UDGCNN is optimized by integrating the Encoder-Decoder structure and skip-connection to extract hierarchical features from the point cloud. The accuracy and efficiency of the UDGCNN is examined by training and testing on 371 LAA geometries from patients with AF, the largest patient-specific dataset in the literature. Using mean absolute error and model inference time as metrics, we demonstrate that UDGCNN can provide an assessment of multiple hemodynamic metrics in 3D patient-specific geometries with prediction error ∼30% lower than those of the state-of-art PointNet model, whereas the inference time is 500-fold shorter compared to computational time CFD simulation. It is noted that UDGCNN is a general computational framework that can be extended to study various cardiovascular diseases. In summary, this study presents a new computational tool that enables end-to-end stratification of thrombotic risk in LAA based on bioimaging, thereby advancing the current screening approach in clinical practice.