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Updated: Jul 17, 2026
Morphological and Functional Assessment of the Right Ventricle Using 3D Echocardiography
Published on: October 28, 2020
AI Learning for Pediatric Right Ventricular Assessment: Development and Validation Across Multiple Centers
Insights
An AI tool automates right ventricular (RV) function assessment in children, achieving expert-level accuracy. This technology improves diagnosis and management of pediatric heart conditions, especially in underserved areas.
Area of Science:
- Cardiology
- Artificial Intelligence in Medicine
- Pediatric Cardiovascular Health
Background:
- Congenital and acquired heart disease impacts approximately 1% of children globally, with right ventricular (RV) dysfunction posing a significant clinical challenge.
- Accurate RV assessment in pediatric patients is complex due to unique cardiac geometry, interventricular interactions, and morphological variability.
- Fractional area change (FAC) is a crucial echocardiographic metric for assessing RV function severity and guiding treatment in pediatric heart disease.
Purpose of the Study:
- To develop and validate an artificial intelligence (AI) framework for automated RV assessment in pediatric echocardiograms.
- To enhance the accuracy and standardization of RV function quantification, including fractional area change (FAC).
- To extend the AI framework's capabilities to improve left ventricular (LV) functional assessment and cardiac abnormality identification, such as pulmonary hypertension (PH).
Main Methods:
- Utilized a large dataset of 24,984 echocardiograms from 3,993 pediatric patients across North American and Asian tertiary care centers.
- Developed a multi-task learning AI framework for automated RV segmentation, beat-by-beat RV FAC quantification, and PH identification.
- Validated the AI framework's performance against expert annotations and across diverse patient cohorts.
Main Results:
- The AI system achieved high Dice similarity coefficients for RV segmentation (0.86 A4C, 0.88 PSAX), comparable to expert performance.
- Demonstrated robust RV functional assessment with Area Under the Curve (AUC) values of 0.95 (U.S.) and 0.97 (Asian cohort).
- Achieved high diagnostic accuracies for PH classification (0.95 U.S., 0.94 Asian) and significantly improved LV ejection fraction (EF) prediction.
Conclusions:
- The validated AI framework provides reliable, automated ventricular function analysis at an expert level for pediatric patients.
- This technology has the potential to streamline clinical workflows and standardize cardiac assessments, improving care for pediatric cardiovascular disorders.
- The AI framework offers particular benefits for improving pediatric cardiac care in resource-limited settings.
Background:
Congenital and acquired heart disease affects ∼1% of children globally, with right ventricular (RV) dysfunction being a common and complex issue due to conditions like congenital heart disease (CHD), pulmonary hypertension (PH), and prematurity. Accurate RV assessment is challenging due to its unique geometry, interventricular interactions, and morphological variability in pediatric patients. Fractional area change (FAC), a key echocardiographic measure, correlates strongly with disease severity, aiding in timely intervention and prognosis. AI learning shows the potential to automate and standardize RV assessments, overcoming traditional limitations and improving early diagnosis and management of pediatric cardiovascular disorders.
Methods:
Using 24,984 echocardiograms from 3,993 pediatric patients across four tertiary care centers (one in North America, three in Asia), we developed and validated an AI framework for automated RV assessment. The framework employs multi-task learning to perform ventricular segmentation, beat-by-beat quantification of RV FAC, and identification of cardiac abnormalities like PH. It was also extended to enhance left ventricular (LV) functional assessment.
Findings:
Our AI system achieved Dice similarity coefficients of 0.86 (apical-four-chamber, A4C) and 0.88 (parasternal-short-axis, PSAX) for RV segmentation, matching expert annotations. It demonstrated robust RV functional assessment, with AUCs of 0.95 (U.S. cohort) and 0.97 (Asian cohort). For PH classification, diagnostic accuracies were 0.95 (U.S.) and 0.94 (Asian), confirming consistent performance across populations. When extended to LV assessment, the framework significantly improved LV ejection fraction (EF) prediction in both U.S. and Asian cohorts.
Interpretation:
This validated AI framework enables reliable, automated ventricular function analysis, matching expert-level performance. By enhancing clinical workflows and standardizing pediatric cardiac assessments, it has the potential to improve care management for pediatric cardiovascular disorders, particularly in resource-limited settings.
Funding:
This work was supported by the U.S. NIH 1R41HL160362-01 to XBL and K23HL150279 to AT.
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