TEENet: An Effective Clinical Detection Network for Identifying Spontaneous Echo Contrast Automatically.

Abstract

Spontaneous Echo Contrast (SEC) is a swirling smoke-like echo phenomenon in Transesophageal Echocardiography (TEE) videos caused by slow blood flow and hypercoagulable states. It is a significant indicator for assessing thromboembolic risk. However, current SEC identification requires extensive manual intervention, leading to low accuracy, high costs, and subjectivity. To address these issues, we propose TEENet, an effective clinical detection network for identifying SEC in TEE videos. Specifically, TEENet first generates attention maps for the input clips to highlight important regions and integrates Convolutional Neural Network with the Multi-Head Self-Attention to capture spatiotemporal representations. Furthermore, to enhance the classification performance across different SEC severity grades, we introduce an auxiliary classification module, which simultaneously utilizes the main classification head and auxiliary classification heads. Notably, we constructed a comprehensive dataset of 1106 TEE videos collected during clinical examinations performed at the First Affiliated Hospital of Soochow University from 2018 to 2023, providing a solid foundation for the development and validation of TEENet. Extensive experimental results demonstrate that our proposed network achieves the highest SEC identification accuracy of 92.4$\pm$1.3% compared to other spatiotemporal representation networks such as SlowFastR50 (89.6$\pm$0.7%) and TimeSformer (74.9$\pm$1.8%), which shows strong potential for effective auxiliary diagnosis in clinical practice.