Digital profile of children's hearts: automated echocardiogram strain analysis facilitates earlier detection of cardiac dysfunction

Clinical Snapshot

Key Findings

• P Value: DeLong P < 0.001 for LVEF decline prediction superiority

• Effect Size: Mean absolute errors: 2.099% for GLS, 2.665% for GCS

• Primary Outcome: Automated strain analysis accuracy compared to manual assessment

• Nnt Or Sensitivity: Correlation coefficients: 0.799 (95% CI 0.715-0.871) for GLS, 0.781 (95% CI 0.687-0.844) for GCS

• Confidence Interval: GLS: 95% CI 1.803-2.401; GCS: 95% CI 2.339-3.026

Clinical Application

Vendor-agnostic system suggests implementation feasibility across different echocardiography platforms in clinical practice Relevant to Australian paediatric cardiology services; potential integration with existing echocardiography workflows in public and private hospitals; may support RACGP guidelines for cardiac monitoring in high-risk children Paediatric patients requiring echocardiographic strain analysis, particularly those at risk of cardiac dysfunction from cancer therapy or congenital heart disease

Abstract

BACKGROUND AND AIMS: Paediatric myocardial strain analysis through echocardiography is often characterized by high variance and limited precision, highlighting the need for a standardized and vendor-agnostic approach applicable for diverse image qualities and populations, which could enhance cardiac function evaluation and enable early detection of cardiac impairment. METHODS: The Motion-Echo system was proposed, a semi-supervised deep learning framework built on 11 096 paediatric and 11 297 adult echocardiograms spanning diverse image qualities and vendors. It integrated context compensation and motion estimation modules for temporally coherent segmentation, myocardial motion estimation, and global strain assessment with minimal manual annotations. Clinical utility was further evaluated through downstream applications. RESULTS: Motion-Echo achieved mean absolute errors of 2.099% [95% confidence interval (CI) 1.803-2.401] and 2.665% (95% CI 2.339-3.026) for global longitudinal and circumferential strain assessments, with Pearson correlation coefficients of 0.799 (95% CI 0.715-0.871) and 0.781 (95% CI 0.687-0.844), respectively. To validate the clinical utility, automated strain values achieved an area under the curve (AUC) of 0.906 (95% CI 0.816-0.981) for cancer therapy-related cardiac dysfunction risk prediction. For late gadolinium enhancement detection, automated global longitudinal strain reached an AUC of 0.782 (95% CI 0.666-0.885). For left ventricular ejection fraction decline forecasting, the system outperformed manual strain values (DeLong P < .001). In addition, incorporating estimated motion flows yielded a remarkable AUC improvement to 0.952 (95% CI 0.917-0.980) for myocardial infarction detection. CONCLUSIONS: Leveraging a large-scale paediatric dataset, Motion-Echo provided a reliable and generalizable framework for myocardial strain analysis, demonstrating potential to facilitate earlier detection of cardiac dysfunction and generate digital cardiac function profiles of children.

References

1. 1.Jiao, R., Liu, X., Shao, S., Zhou, K., Zhao, L., Pu, B., Hua, Y., Guo, X., Cai, X., Zhang, L., Chen, X., Yue, F., Wang, Y., Yuan, Y., Xue, B., Wang, R., Wang, C., Zu, W., Chen, L., Wang, Y., Zhang, Y., Wang, C., & Jin, C. (2026). Digital profile of children's hearts: automated echocardiogram strain analysis facilitates earlier detection of cardiac dysfunction. European Heart Journal. https://doi.org/10.1093/eurheartj/ehaf952