A deep learning framework for standardized interpretation of multiparameter cardiac ultrasound and disease classification

Automated Detection of Clinically Significant Mitral Regurgitation from Single-View B-mode Echocardiography Using Deep Learning.

MitralVision reliably distinguishes clinically significant MR using single-view B-mode echocardiography without Doppler input for model inference and may support more standardized MR screening.

Artificial intelligence for the assessment of diastolic function.

This review summarizes recent advances in the use of AI to facilitate diastolic function assessment and addresses limitations of AI including explainability, generalizability, regulatory considerations, and integration into clinical workflows.

BackMix-Enhanced Semi-Supervised Learning for Automated Detection of Aortic Stenosis from Transthoracic Echocardiographic Images

The proposed anatomically guided BackMix augmentation combined with semi-supervised ensemble learning can improve classification accuracy, robustness, and interpretability in echocardiographic analysis under limited annotation conditions, offering a promising approach for automated AS assessment across independent clinical datasets.

Machine Learning for Biomedical Imaging Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction

Artificial intelligence-empowered echocardiography: an updated review of clinical management in hypertrophic cardiomyopathy

Overall, AI-empowered echocardiography holds substantial promise for advancing precision diagnosis, risk stratification, and personalized management of HCM, facilitating a transition toward more intelligent and individualized cardiovascular care.

Beyond Doppler: Scalable AI Detection of LVOT Obstruction in HCM.

BACKGROUND Accurate assessment of left ventricular outflow tract (LVOT) gradients is critical for hypertrophic cardiomyopathy management, yet Doppler-based measurements are technically demanding and require expertise. The objective of this work was to develop a multi-view deep learning model capable of classifying LVOT obstruction (>20 mm Hg) using routine 2-dimensional echocardiographic windows without reliance on Doppler imaging. METHODS We trained and externally validated a cross-attention-based video-to-video fusion framework that integrated EchoPrime-derived video representations from 3 standard transthoracic echocardiographic views to classify LVOT gradients. RESULTS Training was performed on a derivation cohort (N=1833) from a tertiary care system in the United States, with model performance evaluated on an internally held-out test set (N=275) and a Korean external validation cohort (N=46). Single-view baselines showed limited discrimination (external area under the receiver operating curves, 0.47-0.70). Conversely, the domain-specific foundational model (EchoPrime) achieved superior single-view performance (area under the receiver operating curves, 0.75-0.80 internal; 0.79-0.83 external), highlighting the importance of echo-specific pretraining and temporal modeling. The proposed multi-view fusion further enhanced predictive performance, with the late fusion model reaching an area under the receiver operating curve of 0.84 on the external cohort with significant population-shift. CONCLUSIONS These results suggest LVOT physiology is encoded in routine 2-dimensional imaging and can be leveraged for clinically relevant gradient classification without Doppler input. The proposed artificial intelligence-guided strategy demonstrates substantial cost savings compared with the screen-all approach. By integrating complementary spatial-temporal information across multiple views, our approach generalizes robustly across populations and may enable real-time decision support, extend LVOT assessment to portable or resource-limited settings, and complement Doppler-based evaluation for longitudinal hypertrophic cardiomyopathy management.