Evidence map›Paper›PMID 40914895›Full record

ArticleJournal of the American College of Cardiology2025

Artificial Intelligence Automation of Echocardiographic Measurements.

Yuki Sahashi, Hirotaka Ieki, Victoria Yuan, Matthew Christensen, Milos Vukadinovic, Christina Binder-Rodriguez, Justin Rhee, James Y Zou, Bryan He, Paul Cheng and 1 more

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of the American College of Cardiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Observational
  4. Review
  5. Article
  6. EchoAgent: guideline-centric reasoning agent for echocardiography measurement and interpretation.International journal of computer assisted radiology and surgery · 2026
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  18. Between hope and hype: assessing artificial intelligence in cardiovascular imaging.European heart journal. Imaging methods and practice · 2025
    Article
  19. Comprehensive aortic stenosis characterization using multi-view deep learning.medRxiv : the preprint server for health sciences · 2025
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  20. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Yuki SahashiDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Hirotaka IekiDivision of Cardiology, Department of Medicine, Stanford University, Palo Alto, California, USA.
Victoria YuanDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; School of Medicine, University of California, Los Angeles, California, USA.
Matthew ChristensenDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Milos VukadinovicDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Bioengineering, University of California Los Angeles, Los Angeles, California, USA; Division of Research, Kaiser Permanente Northern California, Pleasanton, California, USA.
Christina Binder-RodriguezDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Justin RheeSchool of Medicine, Brown University, Providence, Rhode Island, USA.
James Y ZouDepartment of Computer Science, Stanford University, Stanford, California, USA; Department of Biomedical Data Science, Stanford University, Stanford, California, USA; Department of Electrical Engineering, Stanford University, Stanford, California, USA.
Bryan HeDepartment of Computer Science, Stanford University, Stanford, California, USA.
Paul ChengDivision of Cardiology, Department of Medicine, Stanford University, Palo Alto, California, USA.
David OuyangDepartment of Cardiology, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA; Department of Cardiology, Kaiser Permanente Santa Clara Medical Center, Santa Clara, California, USA. Electronic address: David.ouyang@kp.org.

Funding

Deep Learning Based Phenotyping and Outcomes Prediction for Valvular Heart DiseaseR01HL173526 · NHLBI · CEDARS-SINAI MEDICAL CENTER · PI David Ouyang · 2024 to 2026
$2.5M
Artificial Intelligence Imaging Biomarkers of Longitudinal Cardiovascular StressR01HL173487 · NHLBI · KAISER FOUNDATION RESEARCH INSTITUTE · PI David Ouyang · 2025 to 2026
$1.4M
Deep Learning Assessment of the Right Ventricle: Function, Etiology, and PrognosisR00HL157421 · NHLBI · KAISER FOUNDATION RESEARCH INSTITUTE · PI OUYANG, DAVID · 2023 to 2025
$747k
NHLBI NIH HHS R00 HL157421NHLBI NIH HHS R01 HL173487NHLBI NIH HHS R01 HL173526
6 · The paper itself

Abstract

backgroundAccurate measurement of echocardiographic parameters is crucial for the diagnosis of cardiovascular disease and tracking of change over time; however, manual assessment requires time-consuming effort and can be imprecise. Artificial intelligence has the potential to reduce clinician burden by automating the time-intensive task of comprehensive measurement of echocardiographic parameters.

objectivesThe purpose of this study was to develop and validate open-sourced deep learning semantic segmentation models for the automated measurement of 18 anatomic and Doppler measurements in echocardiography.

methodsWe trained models for the automated measurement of echocardiography parameters using data sets between 2011 and 2023 from Cedars-Sinai Medical Center (CSMC). The outputs of segmentation models were compared with sonographer measurements from temporal split data from CSMC and an external data set from Stanford Healthcare (SHC) to access accuracy and precision.

resultsWe used 877,983 echocardiographic measurements from 155,215 studies from CSMC to develop EchoNet-Measurements, an open-source deep learning model for echocardiographic annotation. The models demonstrated high accuracy when compared with sonographer measurements from held-out data from CSMC and an independent external validation data set from SHC. Measurements across all 9 B-mode and 9 Doppler measurements had high accuracy (mean coverage probability of 0.796 and 0.839 and mean relative difference of 0.120 and 0.096 on held-out test set from CSMC and external data set from SHC, respectively). When evaluated end-to-end on 2,103 temporally distinct studies at CSMC, EchoNet-Measurements had similar reasonable performance (mean coverage probability 0.803 and mean relative difference of 0.108). Performance was consistent across patient characteristics including age, sex, and atrial fibrillation, obesity status, and machine vendors.

conclusionsEchoNet-Measurements achieves high accuracy in automated echocardiographic quantification and potential for assisting the clinicians in the echocardiography workflow. This open-source model provides the foundation for future developments in artificial intelligence applied to echocardiography.

Indexed as

Artificial IntelligenceCardiovascular DiseasesDeep LearningEchocardiographyAutomationFemaleHumansMaleMiddle Agedautomated measurementconvolutional neural networkdeep learningDoppler waveechocardiography

Identifiers

PMID40914895

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.