The paper introduces the first publicly available dataset of over 25,000 parasternal long‑axis (PLAX) echocardiography videos labeled for left ventricular ejection fraction (EF), created through a novel data‑generation strategy that links clinical notes to video data. Using this dataset, the authors train a reproducible PLAX‑based EF model that achieves a mean absolute error (MAE) of 6.86%, comparable to the clinical standard of apical four‑chamber (A4C) methods. They further show that simple late fusion of PLAX and A4C predictions reduces MAE to 6.37%, highlighting the benefit of multi‑view integration, and release the dataset, models, and demos publicly.
By Zhiyuan Gao, Dominic Yurk, Yaser S. Abu-Mostafa
The paper introduces the first publicly available dataset for predicting left ventricular ejection fraction (EF) from parasternal long-axis (PLAX) echocardiography, comprising over 25,000 labeled videos generated through a novel data‑generation strategy that correlates clinical notes with echocardiographic videos. Using this dataset, the authors train a reproducible PLAX‑EF model that achieves a mean absolute error (MAE) of 6.86%, comparable to the clinical standard of apical four‑chamber (A4C) methods. They further show that combining PLAX and A4C predictions via simple late fusion reduces MAE to 6.37%, highlighting the benefit of multi‑view integration, and they release the dataset, models, and demos for community use.
The paper introduces a self‑supervised method for detecting end‑diastole (ED) and end‑systole (ES) in echocardiography by constraining the latent motion to a single‑parameter orbit, effectively modeling cardiac phase as a one‑dimensional signal. This approach yields an interpretable representation that directly identifies ED and ES, improving ED localisation and matching ES performance compared to prior state‑of‑the‑art methods, while using fewer training epochs and a more constrained model. The method is trained on EchoNet‑Dynamic without annotations and the code is publicly available.
By John Bonnici, Matthew Baugh, Aleksandra Kulbaka, Sarah Cechnicka, Bernhard Kainz, Alberto Gomez
EchoXFlow is a new clinical echocardiography dataset that preserves ultrasound recordings in their native acquisition geometry, offering 35,377 recordings from 666 routine-care examinations of 622 patients. Unlike existing datasets, it retains separate modality streams—including temporally resolved 1D, 2D, and 3D data, multiple Doppler modalities, and synchronized ECG—along with clinical annotations ranging from guideline-defined measurements to dense 2D myocardial contours and 3D left‑ventricular endocardial meshes. The accompanying open‑source tooling enables cross‑modal, acquisition‑aware learning tasks that are impossible with conventional scan‑converted videos, positioning EchoXFlow as a testbed for 4D vision and physically grounded multi‑modal learning.
By Elias Stenhede, Joanna Sulkowska, Eivind Bj{\o}rkan Orstad, Henrik Schirmer, Arian Ranjbar
arXiv:2605. 16427v2 Announce Type: replace-cross Abstract: Deep learning models for echocardiography segmentation often struggle to generalise across institutions, scanners, and patient populations, where collecting large, consistently annotated datasets is infeasible.
By Soroush Elyasi, Sara Adibzadeh, Nasim Dadashi Serej, Massoud Zolgharni
arXiv:2609.08043v1 Announce Type: cross
Abstract: Deep learning has achieved state-of-the-art performance in echocardiographic video segmentation, with an increasing number of models incorporating te...
By Jiyoo Noh, Jonathan H. Chan