Echo-EU-Net: Lightweight Deep Learning for Fully Automated Left Ventricular Segmentation and Ejection Fraction Quantification in Transgastric Short-Axis View on Transesophageal Echocardiography.

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.

ESPNet: An Automated Left Ventricular Ejection Fraction Measurement via Multi-Frame Temporal Aggregation.

The results demonstrate that ESPNet not only outperforms existing methods but also achieves a level of consistency comparable to inter observer variability among mid-career clinicians, effectively addressing the clinical need for reliable and efficient automated LVEF measurement.

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

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.

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

Automated whole-heart volumetrics and haemodynamics from 4D flow CMR magnitude images: development and validation of a deep learning model

Abstract Aims 4D flow cardiovascular magnetic resonance (CMR) offers a comprehensive haemodynamic assessment but is often limited by long acquisition times and complex post-processing. The magnitude images derived from 4D flow sequences contain time-resolved 3D anatomical information. We aimed to validate the anatomical accuracy of these images against standard cine imaging and develop an artificial intelligence (AI) model for automated segmentation to facilitate analysis. Methods and results Forty patients prospectively identified from the PREFER-CMR registry underwent CMR, including standard cine stacks and 4D flow. The study consisted of two stages. In Stage 1, manual segmentation of the cardiac chambers and great vessels was performed on 4D flow magnitude images. These were validated against standard cine volumetrics (LV/RV) and normative reference values (LA/RA). In Stage 2, a fully automated deep learning algorithm was trained and validated. Advanced haemodynamic metrics were derived using both manual and AI segmentations to assess agreement. The study cohort (n = 40) had a mean age of 69.0 ± 17.2 years, and 60.0% were male. In Stage 1, 4D flow magnitude analysis demonstrated excellent correlations with cine measurements for LV end-diastolic volume (ρ = 0.98, ICC = 0.99) and RV end-diastolic volume (ρ = 0.97, ICC = 0.98). In Stage 2, the AI model achieved excellent segmentation performance (mean Dice similarity coefficient 0.88). Comparisons of haemodynamic metrics derived from AI vs. manual contours showed strong agreement (r ≥ 0.88 for all peak metrics). Conclusion 4D flow magnitude imaging provides accurate volumetrics. Deep learning automation of this process is feasible, allowing for rapid, comprehensive assessment of cardiac structure, function, and advanced energetics.

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.