ArticleJournal of advanced research2024
Chamber Attention Network (CAN): Towards interpretable diagnosis of pulmonary artery hypertension using echocardiography.
Article in Journal of advanced research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Single-view echocardiography-based deep learning for pulmonary hypertension detection: a multi-level supervised and explainable approach.Quantitative imaging in medicine and surgery · 2026Article
- Artificial Intelligence in Pulmonary Hypertension: Current State and Future Prospects.Reviews in cardiovascular medicine · 2026Review
- The role of artificial intelligence and mobile health in diagnosis and management of pulmonary arterial hypertension.International journal of cardiology. Congenital heart disease · 2025Review
- Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features.BMC medical imaging · 2025Article
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17 authors.
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Abstract
introductionAccurate 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.
objectivesOur 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.
methodsWe 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.
resultsThe 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.
conclusionsThese 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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