Chamber Attention Network (CAN): Towards interpretable diagnosis of pulmonary artery hypertension using echocardiography

作者：Dezhi Sun, Yangyi Hu, Yunming Li, Xianbiao Yu, Xi Chen, Pan Shen, Xianglin Tang, Yihao Wang, Chengcai Lai, Bo Gyeong Kang, Zhijie Bai, Zhexin Ni, Ning Ning Wang, Rui Wang, Lina Guan, Wei Zhou, Yue Gao · 发表于：Journal of Advanced Research · 年份：2023 · DOI：10.1016/j.jare.2023.10.013 · 被引用次数：15 · 研究领域：Pulmonary Hypertension Research and Treatments、Phonocardiography and Auscultation Techniques、Cardiovascular Disease and Adiposity

INTRODUCTION: Accurate identification of pulmonary arterial hypertension (PAH) in primary care and rural areas can be a challenging task. However, recent advancements in computer vision offer the potential for automated systems to detect PAH from echocardiography. OBJECTIVES: Our aim was to develop a precise and efficient diagnostic model for PAH tailored to the unique requirements of intelligent diagnosis, especially in challenging locales like high-altitude regions. METHODS: We proposed the Chamber Attention Network (CAN) for PAH identification from echocardiographic images, trained on a dataset comprising 13,912 individual subjects. A convolutional neural network (CNN) for view classification was used to select the clinically relevant apical four chamber (A4C) and parasternal long axis (PLAX) views for PAH diagnosis. To assess the importance of different heart chambers in PAH diagnosis, we developed a novel Chamber Attention Module. RESULTS: The experimental results demonstrated that: 1) The substantial correspondence between our obtained chamber attention vector and clinical expertise suggested that our model was highly interpretable, potentially uncovering diagnostic insights overlooked by the clinical community. 2) The proposed CAN model exhibited superior image-level accuracy and faster convergence on the internal validation dataset compared to the other four models. Furthermore, our CAN model outperformed the others on the external test dataset, with image-level accur...