胸骨旁线
计算机科学
卷积神经网络
人工智能
鉴定(生物学)
人工神经网络
深度学习
可靠性(半导体)
任务(项目管理)
机器学习
模式识别(心理学)
医学
心脏病学
功率(物理)
植物
物理
管理
量子力学
经济
生物
作者
Dezhi Sun,Yangyi Hu,Yunming Li,Xianchuan Yu,Xi Chen,Shaowei Pan,Xianglin Tang,Yihao Wang,Lai Chen,Bumsoo Kang,Zhijie Bai,Zhen Ni,Ning Ning Wang,Rui Wang,Lei Guan,Wei Zhou,Yue Gao
标识
DOI:10.1016/j.jare.2023.10.013
摘要
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. 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. 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. 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 accuracies of 82.53% and 83.32% for A4C and PLAX, respectively. 3) Implementation of the voting strategy notably enhanced the positive predictive value (PPV) and negative predictive value (NPV) of individual-level classification results, enhancing the reliability of our classification outcomes. These findings indicate that CAN is a feasible technique for AI-assisted PAH diagnosis, providing new insights into cardiac structural changes observed in echocardiography.
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