Cardiovascular Function and Risk Factors

Latest papers

• The segmentation ceiling: why explicit left-ventricular masks do not improve learned ejection-fraction regression

  Farshid Farhadi Khouzani, Paul La Plante, Bryar Mustafa Shareef, Laxmi Gewali · 18 September 2026

  Accurate estimation of left ventricular ejection fraction (EF) from echocardiography is central to cardiovascular care, and deep learning enables automated EF prediction from echocardiographic video. Because EF is clinically derived from left-ventricular (LV) volumes, a widely held intuition is that…

• Self-Supervised Cardiac Phase Detection via Single-Parameter Latent Orbits

  John Bonnici, Matthew Baugh, Aleksandra Kulbaka, Sarah Cechnicka, Bernhard Kainz, Alberto Gomez · 11 September 2026

  Accurate identification of end-diastole (ED) and end-systole (ES) in echocardiography underpins the quantification of ventricular function, yet manual selection of these key frames is subjective and introduces clinically significant inter-operator variability. Recent self-supervised methods either p…

• Myocardial Strain Drift Correction in Deep Learning Based Ultrasound Tracking

  Thierry Judge, Nicolas Duchateau, Andreas {\O}stvik, Havard Dalen, Bj{\o}rnar Grenne, Pierre-Yves Courand, Lasse Lovstakken, Pierre-Marc Jodoin, Olivier Bernard · 10 September 2026

  Myocardial strain from echocardiography is a key biomarker for cardiac function. Recent deep learning methods show strong performance for myocardial motion tracking but often lack physiological constraints, leading to temporal drift across the cardiac cycle. Consequently, tracked points may not retu…

• Learning from Scarce Labels: Multi-View Echocardiography for Ejection Fraction Prediction

  Zhiyuan Gao, Dominic Yurk, Yaser S. Abu-Mostafa · 4 September 2026

  We present, to the best of our knowledge, the first publicly available resource for predicting left ventricular ejection fraction (EF) from parasternal long-axis (PLAX) echocardiography. Because no PLAX-EF datasets previously existed, our work focuses on an innovative data generation strategy to ove…

• Learning to Beat: Phenotype-Guided Latent Flow with Regional Motion Priors for Biventricular Motion Synthesis

  Xuan Yang, Xiaohan Yuan, Hao Li, Lingyu Chen, Yanan Liu, Qingya Li, Lei Li · 21 August 2026

  Full-cycle biventricular geometry is essential for characterizing cardiac function. However, dense and temporally consistent 3D+t biventricular meshes are not routinely available, whereas end-diastolic (ED) anatomy can often be obtained reliably. We therefore investigate full-cycle biventricular mot…

• A Unified DINOv2-Based Framework for LVEF Estimation, GLS Dysfunction Classification, and Early Cardiotoxicity Prediction

  Xiaotong Zhang, Mingyue Cui, Qing Cao, Jingming Xia · 17 August 2026

  Left ventricular ejection fraction (LVEF) estimation (Task 1), global longitu-dinal strain (GLS)-based dysfunction classification (Task 2), and early cardi-otoxicity prediction (Task 3) provide complementary information for cardio-oncology assessment. LVEF reflects macroscopic ventricular volume cha…

• When Oracle Conditioning Misleads Deployment: Conditioning-Availability Bias in Echocardiographic Segmentation

  Dang P. M. Cao, Hieu D. Pham, Hieu Pham · 5 August 2026

  Conditional segmentation models may be trained and evaluated with auxiliary signals cleaner than those available at deployment. We study this protocol-level manifestation of shortcut learning and auxiliary-variable shift in phase-conditioned echocardiographic segmentation. The complementary gap pair…

• When Measurement Conventions Masquerade as Calibration Gains in Cardiac Digital Twins

  Dang P. M. Cao, Hieu Pham · 4 August 2026

  Cardiac digital twins convert clinical images into physiological measurements through observation operators, yet calibration studies often assume a fixed reference convention. Across four shared-backbone echocardiographic EF front-ends, phase conditioning appears to remove CAMUS baseline bias. Match…

• Loss Invariance Determines What Concept Layers Encode: Volume Grounding in Echocardiography

  Hyunkyung Han, Min Jung Kim · 29 July 2026

  Objective: Concept bottleneck models route prediction through interpretable intermediate variables, and their validity is normally judged by how accurately those variables are predicted. We ask whether that judgement is sufficient, using left ventricular volumes as the concepts underlying ejection f…

• Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentation

  Soroush Elyasi, Nasim Dadashi Serej, Julie Wall, Massoud Zolgharni · 23 July 2026

  Cross-dataset generalisation remains a major barrier to clinical deployment of echocardiographic left ventricular segmentation, yet the sources of this shift are rarely disentangled. We examined whether transfer degradation could be estimated before deployment using handcrafted ultrasound descriptor…

• Anatomically Faithful but Temporally Blind: Auditing Attribution for Left-Ventricular Ejection-Fraction Estimation from Echocardiography

  Hyunkyung Han, Min Jung Kim · 16 July 2026

  Background and Objective: Deep video models estimate left-ventricular ejection fraction (EF) from echocardiography with near-expert accuracy, and post-hoc attribution (Chefer relevance for transformers, Grad-CAM for CNNs) is increasingly used to certify that models "look at the right place." Yet whe…

• EAGT: Echocardiography Augmentation for Generalisability and Transferability

  Soroush Elyasi, Sara Adibzadeh, Nasim Dadashi Serej, Massoud Zolgharni · 29 June 2026

  Deep learning models for echocardiography segmentation often struggle to generalise across institutions, scanners, and patient populations, where collecting large, consistently annotated datasets is infeasible. Data augmentation is inexpensive and widely used to improve the robustness of deep learni…

• HypOProto: Hyperbolic Ordinal Prototypes for Left Ventricular Filling Pressure Classification

  Victoria Wu, Nima Hashemi, Hooman Vaseli, Christina Luong, Purang Abolmaesumi, Teresa S. M. Tsang · 19 June 2026

  Echocardiography (echo) is a widely used imaging modality for assessing cardiac function, with Left Ventricular Filling Pressure (LVFP) serving as a critical physiological marker for conditions such as heart failure. Standard LVFP classification into normal \emph{vs} elevated categories relies on th…

• Deep Learning Strain Estimation: Is Physics-Based Simulation the Solution?

  Thierry Judge, Nicolas Duchateau, Andreas {\O}stvik, Khuram Faraz, Anders Austlid Task\'en, Sigve Karlsen, Thor Edvardsen, Harald Brunvand, Md Abulkalam Azad, Havard Dalen, Bj{\o}rnar Grenne, Gabriel Kiss, Pierre-Yves Courand, Lasse Lovstakken, Pierre-Marc Jodoin, Olivier Bernard · 28 May 2026

  Speckle tracking echocardiography (STE) is the clinical standard for myocardial strain estimation. Despite good performance on global strain (GLS), its accuracy for regional strain remains limited, even though this biomarker is highly relevant for early diagnosis and the characterization of subtle a…

• EchoVQA: Enabling Conversational Assistance for Point-of-Care Cardiac Ultrasound

  Filippos Bellos, Yutong Li, Jessie N Dong, Zaiyang Guo, Emily Mackay, Yayuan Li, Yannis Avrithis, Alison Pouch, Jason J. Corso · 26 May 2026

  Point-of-care transthoracic echocardiography (TTE) enables cardiac assessment in virtually any clinical setting, yet its diagnostic utility remains constrained by the expertise required for image acquisition and interpretation. Visual question answering (VQA) offers a promising paradigm for bridging…

• Associations between echocardiographic traits and AI-ECG predictions of heart failure

  Elias Stenhede, Eivind Bj{\o}rkan Orstad, Torbj{\o}rn Omland, Henrik Schirmer, Arian Ranjbar · 26 May 2026

  Artificial intelligence-enabled electrocardiography (AI-ECG) can detect heart failure (HF), including disease not captured by left ventricular ejection fraction (LVEF), but the cardiac phenotypes underlying model predictions remain unclear. We therefore investigated whether AI-ECG-predicted HF risk …

• Reinforcement Learning for Unsupervised Domain Adaptation in Spatio-Temporal Echocardiography Segmentation

  Arnaud Judge, Nicolas Duchateau, Thierry Judge, Roman A. Sandler, Joseph Z. Sokol, Christian Desrosiers, Olivier Bernard, Pierre-Marc Jodoin · 14 May 2026

  Domain adaptation methods aim to bridge the gap between datasets by enabling knowledge transfer across domains, reducing the need for additional expert annotations. However, many approaches struggle with reliability in the target domain, an issue particularly critical in medical image segmentation, …

• EchoXFlow: A Beamspace Echocardiography Dataset for Cardiac Motion, Flow, and Function

  Elias Stenhede, Joanna Sulkowska, Eivind Bjørkan Orstad, Henrik Schirmer, Arian Ranjbar · 8 May 2026

  We introduce EchoXFlow, a clinical echocardiography dataset for learning from ultrasound in its native acquisition geometry rather than from scan-converted Cartesian videos. Existing public datasets offer limited opportunities to study cross-modal relationships between cardiac anatomy, myocardial mo…

• Contour-Guided Query-Based Feature Fusion for Boundary-Aware and Generalizable Cardiac Ultrasound Segmentation

  Zahid Ullah, Sieun Choi, Jihie Kim · 31 March 2026

  Accurate cardiac ultrasound segmentation is essential for reliable assessment of ventricular function in intelligent healthcare systems. However, echocardiographic images are challenging due to low contrast, speckle noise, irregular boundaries, and domain shifts across devices and patient population…

• C2W-Tune: Cavity-to -Wall Transfer Learning for Thin Atrial Wall Segmentation in 3D LGE-MRI

  Yusri Al-Sanaani, Rebecca Thornhill, Sreeraman Rajan · 27 March 2026

  Accurate segmentation of the left atrial (LA) wall in 3D late gadolinium-enhanced MRI (LGE-MRI) is essential for wall thickness mapping and fibrosis quantification, yet it remains challenging due to the wall's thin geometry, complex anatomy, and low contrast. We propose C2W-Tune, a two-stage cavity-…

• ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control

  Ehsan Futuhi, Shayan Karimi, Chao Gao, Martin M\"uller · 18 February 2026

  We consider deep deterministic policy gradient (DDPG) in the context of reinforcement learning with sparse rewards. To enhance exploration, we introduce a search procedure, \emph{${\epsilon}{t}$-greedy}, which generates exploratory options for exploring less-visited states. We prove that search usin…

• Benchmarking Self-Supervised Models for Cardiac Ultrasound View Classification

  Youssef Megahed, Salma I. Megahed, Robin Ducharme, Inok Lee, Adrian D. C. Chan, Mark C. Walker, Steven Hawken · 18 February 2026

  Reliable interpretation of cardiac ultrasound images is essential for accurate clinical diagnosis and assessment. Self-supervised learning has shown promise in medical imaging by leveraging large unlabelled datasets to learn meaningful representations. In this study, we evaluate and compare two self…

• Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data

  Adson Duarte, Davide Vitturini, Emanuele Milillo, Andrea Bragagnolo, Carlo Alberto Barbano, Riccardo Renzulli, Michele Cannito, Federico Giacobbe, Francesco Bruno, Ovidio de Filippo, Fabrizio D'Ascenzo, Marco Grangetto · 17 February 2026

  Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasive and time-consuming procedure, motivating the development of reliable non-invasive alternatives using echocardiography…

• Generative Regression for Left Ventricular Ejection Fraction Estimation from Echocardiography Video

  Jinrong Lv, Xun Gong, Zhaohuan Li, Weili Jiang · 10 February 2026

  Estimating Left Ventricular Ejection Fraction (LVEF) from echocardiograms constitutes an ill-posed inverse problem. Inherent noise, artifacts, and limited viewing angles introduce ambiguity, where a single video sequence may map not to a unique ground truth, but rather to a distribution of plausible…

• Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification

  Jyun-Ping Kao, Jiaxing Yang, C. -C. Jay Kuo, Jonghye Woo · 28 January 2026

  Echocardiography is a cornerstone for managing heart failure (HF), with Left Ventricular Ejection Fraction (LVEF) being a critical metric for guiding therapy. However, manual LVEF assessment suffers from high inter-observer variability, while existing Deep Learning (DL) models are often computationa…