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

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.

Automated left ventricle segmentation using FCNs for quantitative assessment of cardiac indices in cardiovascular disease

Development and multi-dataset evaluation of a unified single-view deep-learning model for the right heart: four-chamber segmentation, biventricular ejection fraction, deformation, and pulmonary-hypertension prediction from the apical four-chamber echocardiogram

Background: Right ventricular (RV) function predicts survival in pulmonary hypertension (PH) and other cardiovascular diseases, yet echocardiographic AI has largely focused on the left ventricle (LV). Objectives: To develop and evaluate PH-ECHO-AI, a unified deep learning model performing four-chamber segmentation, landmark localisation, biventricular ejection fraction (EF) estimation, deformation analysis, and PH prediction from a single apical four-chamber (A4C) clip. Methods: We developed the model using 8,416 clips from four public datasets and no institutional data: EchoNet-Dynamic, CAMUS, RVENet (apical four-chamber clips paired with 3D-echocardiographic right ventricular ejection fraction, RVEF), and MIMIC-IV-ECHO. Evaluation used held-out, training-excluded data with expert-reviewed reference standards and a per-cohort audit of patient-level separation: 1,416 clips for segmentation; 600 clips for function and deformation (350 referenced to 3D-echocardiographic RVEF, 250 to the EchoNet LVEF); and 1,076 MIMIC-IV patients for PH prediction, with five-fold cross-validation. Performance measures were Dice, correlation, mean absolute error (MAE), Bland-Altman agreement, and area under the receiver operating characteristic curve (AUC). Results: Four-chamber segmentation generalised robustly across all datasets (pooled Dice: LV 0.925, RV 0.836, LA 0.910, RA 0.904). Left ventricular ejection fraction (LVEF) was estimated with r=0.845 (95% CI 0.786 to 0.886) and MAE 4.67%. RVEF, regressed directly from the clip by a supervised head trained on 3D-echocardiographic labels with no geometric assumption, reached r=0.754 (95% CI 0.690 to 0.806) and MAE 4.98%, matching published single-view RVEF ceilings and exceeding geometric RV fractional area change (RVFAC; r=0.278). Deformation and excursion metrics, namely RV free-wall and LV A4C longitudinal strain and tricuspid and mitral annular plane systolic excursion (TAPSE, MAPSE), proved physiologically coherent. Segmentation generalised to the external MIMIC-IV cohort, and PH prediction was developed and evaluated entirely within it; RVEF evaluation was clip-disjoint and same-source, so cross-centre RVEF validation remains outstanding. Using echocardiographic geometry alone, confirmed PH was detected with an AUC of 0.697 and strong calibration (Brier 0.061). Conclusions: A single, reproducible model provides comprehensive right-heart-focused interpretation from one A4C view. It achieves RVEF accuracy competitive with dedicated RV models while simultaneously delivering segmentation, deformation, annular excursion (TAPSE and MAPSE), and PH prediction. Registration: This retrospective study used existing datasets. Code is openly released, and trained model weights are available to credentialed investigators, for independent evaluation.

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.

Toward self-contained cardiac magnetic resonance elastography: Deep learning-based segmentation of the left ventricular myocardium.

BACKGROUND Cardiac magnetic resonance elastography (MRE) is an emerging modality for noninvasive assessment of left ventricular (LV) myocardial stiffness. Accurate LV myocardium delineation is essential for MRE analysis, yet current workflows often rely on manual annotation and additional structural MRI. It remains uncertain whether native cardiac MRE data alone are sufficient for reliable automated LV segmentation. PURPOSE To evaluate deep learning approaches for LV myocardium segmentation on cardiac MRE data and to assess the influence of input representation and automation strategy on segmentation performance. METHODS Cardiac MRE data from 16 healthy male volunteers were used to train and evaluate two contemporary segmentation frameworks, nnU-Net v2 and MedSAM. Reader 1 annotated the full dataset using MRE magnitude images, and Reader 2 independently annotated the test set, enabling model performance to be benchmarked against inter-reader agreement. nnU-Net was trained using multiple input representations and training strategies. MedSAM was evaluated in zero-shot, semi-automated, fine-tuned, autoprompt, and box-regression configurations. RESULTS Inter-reader Dice agreement was 0.79 ± 0.03. The best nnU-Net model, trained on fully averaged normalized magnitude images, achieved a Dice score of 0.82 ± 0.04. Performance was lower with magnitude-plus-phase and real-plus-imaginary inputs, with Dice scores of 0.65 ± 0.21 and 0.60 ± 0.20, respectively, and also decreased with non-normalized magnitude input, which yielded a Dice score of 0.75 ± 0.05. The best MedSAM result was obtained with a semi-automated fine-tuned variant using strong ROI smoothing, which achieved a Dice score of 0.82 ± 0.02. Fully automated MedSAM variants performed less well, with Dice scores of 0.68 ± 0.09 for autoprompt and 0.71 ± 0.08 for box regression. CONCLUSIONS Cardiac MRE data alone demonstrated the feasibility of accurate LV myocardium segmentation, with nnU-Net and MedSAM both reaching inter-reader-level performance. These findings support direct segmentation of the LV myocardium from native cardiac MRE and represent a step toward a self-contained cardiac MRE workflow.

Beat-Level Echocardiographic Clip Analysis with Geometry-Video Fusion and Cross–Attention for Accurate Ejection Fraction Estimation and Heart Failure Stratification

Left ventricular ejection fraction (LVEF) is a crucial indicator of cardiac function in the diagnosis and management of heart failure. Recent deep learning approaches have shown promising results for automated LVEF estimation from echocardiographic videos; most studies rely on spatiotemporal information, and few studies investigate the contribution of anatomical descriptors. In this study, we propose a comprehensive beat-level framework for EF estimation and heart failure classification using echocardiographic videos. Representative cardiac beat clips were extracted using tracing-derived annotations. Three architectures were investigated: (a) EchoEF-Net, a spatiotemporal Convolution Neural Network (CNN), which is a video-only model; (b) GeoFusionEF-Net, a multimodal deep learning feature-level fusion of geometry and video features for enhanced robustness and calibration; and (c) CrossAttnFusionEF-Net, which employs a staged cross-attention multimodal architecture to integrate the video and geometry interactions adaptively. Experiments were conducted on the publicly available EchoNet-Dynamic dataset (10,030 videos). On the official test set, the proposed GeoFusionEF-Net model effectively captures interactions between the video and geometry modalities, significantly reducing mean absolute error from 4.61 (EchoEF-Net) and 1.99 (CrossAttnFusionEF-Net) to 0.648, while improving root mean squared error and Pearson correlation to 1.413 and 0.99, respectively. The estimated EF predictions were further utilized to automate heart failure stratification into reduced (HFrEF), mid-range (HFmrEF), and preserved (HFpEF) EF categories. The meta-classifier of all three EF estimators achieved 0.97 accuracy and 0.99 Area Under the Curve (AUC). The proposed framework, combining beat-level motion analysis and geometric features with Shapley Additive Explanations- and Gradient-Weighted Class Activation Mapping-based explainability, enables quantification of ejection fraction and clinically reliable heart failure classification, thereby scaling echocardiographic decision-making.   Received: 2 April 2026 | Revised: 15 June 2026 | Accepted: 24 June 2026   Conflicts of Interest The authors declare that they have no conflicts of interest to this work.   Data Availability Statement The data that support the findings of this study are openly available in the EchoNet-Dynamic repository at https://stanfordaimi.azurewebsites.net/datasets/834e1cd1-92f7-4268-9daa-d359198b310a.   Author Contribution Statement Chaithra C. S.: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Siddesha S.: Conceptualization, Validation, Formal analysis, Investigation, Writing – review & editing, Supervision, Project administration. Vinay Kumar N.: Validation, Formal analysis, Writing – review & editing. V. N. Manjunath Aradhya: Validation, Formal analysis, Writing – review & editing.