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.
Echo-E$^3$Net is an anatomy‑guided spatio‑temporal neural network designed to estimate left ventricular ejection fraction (LVEF) from ultrasound images. It uses a dual‑phase Endocardial Border Detector to locate end‑diastole and end‑systole landmarks and an Endocardial Feature Aggregator to fuse these landmarks with global deep‑feature descriptors for EF regression. The model achieves competitive accuracy on EchoNet‑Dynamic and EchoNet‑Pediatric datasets while using only 1.55 M parameters and 8.05 GFLOPs, enabling real‑time deployment on limited‑resource devices.
By Moein Heidari, Afshin Bozorgpour, AmirHossein Zarif-Fakharnia, Wenjin Chen, Dorit Merhof, David J. Foran, Jasmine Grewal, Ilker Hacihaliloglu
arXiv:2606. 17437v1 Announce Type: cross Abstract: Automated classification of standard echocardiographic views is crucial for efficient clinical workflow but faces three main challenges.
By Bo Gou, Jicheng Zhang, Jianlong Xiong, Tao He, Bentian Liu, Hai Wu, Yijiao Wang, Yu Zhang, Yujia Yang, Yun Dai, Jian Liu, Jie Wang
arXiv:2606. 28164v1 Announce Type: cross Abstract: Echocardiography is the most widely used non-invasive cardiac imaging modality, providing essential information for cardiovascular diagnosis.
By Darya Taratynova, Ahmed Aly, Numan Saeed, Mohammad Yaqub
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
The paper introduces the "segmentation ceiling," a quantitative criterion that determines when explicit left‑ventricular (LV) segmentation can improve ejection fraction (EF) regression. By deriving how per‑frame segmentation area error propagates into EF error, the authors find that a break‑even error of about 10% per frame is required, whereas typical segmenters exceed this threshold (~14%). Consequently, several strategies that incorporate segmentation or area information fail to outperform a raw‑video baseline, while techniques such as weight averaging with strong augmentation and a heteroscedastic beta‑NLL loss yield competitive EF predictions and well‑calibrated uncertainty estimates.
By Farshid Farhadi Khouzani, Paul La Plante, Bryar Mustafa Shareef, Laxmi Gewali
arXiv:2610.05603v2 Announce Type: replace
Abstract: Echocardiography is the most widely used cardiac imaging modality, yet interpretation demands integrating visual evidence across global anatomy, lo...
By Sheng Cheng, Donnchadh M. O'Sullivan, Daniel J. Penny, Craig G. Rusin, Minh B. Nguyen, Devika Subramanian
arXiv:2607. 01039v1 Announce Type: cross Abstract: Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardiac imaging remains an unsolved problem.
By Grigorios Kalliatakis, Georgia Karanasiou, Georgios Manikis, Manolis Tsiknakis, Dimitrios Fotiadis, Dorothea Tsekoura, Kalliopi Keramida, Vasileios Bouratzis, Lampros Lakkas, Katerina Naka, Andri Papakonstantinou, Anastasia Constantinidou, Kostas Marias
arXiv:2607. 25748v1 Announce Type: new Abstract: Objective: Concept bottleneck models route prediction through interpretable intermediate variables, and their validity is normally judged by how accurately those variables are predicted.
By Hyunkyung Han, Min Jung Kim
Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardiac imaging remains an unsolved problem. We present EchoRisk, the first curated, multicentre, longitudinal echocardiography dataset with explicit cardiotoxicity labels, released as the primary technical reference for the EchoRisk-MICCAI 2026 challenge.
arXiv:2603. 08505v2 Announce Type: replace-cross Abstract: Electrocardiography (ECG) is a low-cost, widely used modality for diagnosing electrical abnormalities like atrial fibrillation by capturing the heart's electrical activity.
By Michelle Espranita Liman, \"Ozg\"un Turgut, Alexander M\"uller, Eimo Martens, Daniel Rueckert, Philip M\"uller
The paper presents a supervised large language model (LLM) that performs late fusion of heterogeneous echocardiography AI outputs directly in language space. By aggregating view‑specific predicted findings and measurements, the LLM learns a case‑level fusion function without requiring conversion to binary decisions or feature engineering. In a patient‑level three‑fold cross‑validation on 859 examinations, the method matched the best reference strategy and surpassed a general‑purpose LLM that lacked task‑specific training.
By Ryo Takizawa, Tempei Kabayama, Chiemi Yamazaki, Shun Kitamura, Ryo Matsuoka, Yuta Ando, Satoshi Kodera, Norihiko Takeda