Artificial intelligence in echocardiography for valvular heart disease.

Abstract

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.