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Mingxing Xie

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Open access Aug 2026

Dual-flow convolutional neural network for automatic measurement of left ventricular ejection fraction and global longitudinal strain in echocardiography

Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are essential for the diagnosis, clinical decision-making, and prognosis of cardiovascular disease. However, accurate assessments of LVEF and GLS by echocardiography are hampered by inter-observer variability, time-consuming, and labor-intensive. This study aimed to develop an automated method to accurately and rapidly assess LVEF and GLS. Based on the datasets of 500 patients (1,500 videos) from the internal center and 363 patients (1,089 videos) from four external centers, we successfully developed a dual-flow convolutional neural network called Echo-DFCNN, which allowed for synchronous acquisition of LVEF and GLS. We evaluated the performance of the Echo-DFCNN in a cardiac magnetic resonance (CMR) validation dataset composed of 67 patients. On the internal test dataset, the AI and manual measurements of LVEF demonstrated a median absolute error of 3.02% and a mean absolute error of 3.94%. AI-predicted LVEF showed good agreement with manually measured LVEF, with an ICC of 0.927, a bias of 0.89%, and a LOA of -10.91 to 12.69. For GLS, the median absolute error and mean absolute error between AI and manual measurements were 1.43% and 1.83%. AI-predicted GLS exhibited high agreement with manually measured GLS (ICC = 0.913; bias = -1.22%, LOA = -5.12 to 2.68). In addition, Echo-DFCNN maintained good performance when applied to external validation datasets. In the CMR validation dataset, the AI model showed good agreement with CMR measurements for both LVEF and GLS. Echo-DFCNN achieves simultaneous and precise assessment of LVEF and GLS in the study cohorts, demonstrating its potential for robust performance across a wide range of cardiac functions, different image qualities, and machine types.

Zisang Zhang, Ye Zhu, Shujun Chen et al. · 0 citations
Review Jul 2026

Artificial Intelligence in Echocardiography for Valvular Heart Disease.

The global burden of valvular heart disease (VHD) is increasingly burdensome, and precise early diagnosis combined with accurate risk stratification constitutes the core strategy for improving patient prognosis. As the first-line imaging modality for VHD assessment, echocardiography is constrained by interobserver variability and cumbersome, time-consuming data processing workflows, which prevent it from fully meeting the demands of precision medicine. In recent years, breakthroughs in artificial intelligence (AI), particularly deep learning (DL) technologies, have been reshaping the paradigm of imaging-based evaluation for VHD. This review systematically summarizes the latest advances in AI applications across the entire workflow of echocardiographic assessment in VHD: from the precise segmentation of valvular anatomical structures and identification of lesions using convolutional neural networks, to the automated grading of hemodynamic severity achieved through end-to-end learning. More importantly, this article explores how AI can surpass the limitations of traditional imaging indicators by leveraging unsupervised clustering to unearth potential high-risk phenotypes and integrating multimodal data to predict adverse outcomes. Finally, the paper critically analyzes the current challenges in data standardization, model interpretability, and clinical translation, and offers perspectives on future directions in the intersection of clinical medicine and engineering.

Xianyu Ke, Ruize Zhang, Jiawei Shi et al. · 0 citations