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Related Experiment Video

Updated: Feb 7, 2026

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Artificial Intelligence-Enabled Echocardiographic Assessment of Right Ventricular Function.

Márton Tokodi1,2, Bryan He3, Ádám Szijártó1

  • 1Heart and Vascular Center, Semmelweis University, Budapest, Hungary.

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Summary

A new deep learning model, EchoNet-RV, accurately assesses right ventricular (RV) function using echocardiograms. This tool provides rapid, automated RV fractional area change (RVFAC) estimation, outperforming human variability and aiding disease surveillance.

Keywords:
artificial intelligencedeep learningechocardiographyright ventricleright ventricular fractional area change

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Area of Science:

  • Cardiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Right ventricular (RV) function is crucial for cardiovascular health but challenging to assess via echocardiography due to complex anatomy and location.
  • Limited inter-observer reproducibility hinders accurate RV function evaluation.

Purpose of the Study:

  • To develop EchoNet-RV, a novel deep learning model for automated RV segmentation and RV fractional area change (RVFAC) estimation from echocardiographic videos.
  • To evaluate EchoNet-RV's performance against expert annotations and existing models on diverse datasets.

Main Methods:

  • Trained EchoNet-RV on 7,169 expert-annotated apical 4-chamber view (A4C) echocardiographic videos.
  • Validated the model on internal (1,320 videos) and two international external test sets (3,107 and 1,077 videos).
  • Assessed the association between predicted RVFAC and clinical outcomes (heart failure hospitalization or all-cause death).

Main Results:

  • EchoNet-RV achieved high Dice coefficients for RV segmentation (0.788-0.893) and low mean absolute errors for RVFAC estimation (5.795-6.362 percentage points).
  • Prediction error was significantly lower than inter-observer variability (p<0.001).
  • The model demonstrated strong performance in identifying RV dysfunction (AUCs 0.684-0.859) and outperformed two multi-task models.

Conclusions:

  • EchoNet-RV offers rapid, automated RVFAC assessment with performance exceeding inter-observer variability.
  • The model shows significant potential as a valuable tool for RV function evaluation and disease surveillance in clinical practice.
  • Predicted RVFAC values were inversely associated with adverse clinical outcomes, highlighting prognostic value.