Abstract
Prenatal assessment of fetal cardiac function is crucial for predicting neonatal outcomes, yet manual echocardiographic measurements are labor-intensive and subjective. We developed a fully automated artificial intelligence (AI) workflow for estimating fetal cardiac function parameters from echocardiograms. The workflow integrates a deep learning model for real-time detection and segmentation of cardiac structures, followed by quality control and geometric calculation. It was developed and validated using an internal dataset of 52,942 annotated images from 1940 normal fetal echocardiograms, with further testing on two external normal datasets (245 echocardiograms) and one internal abnormal dataset (83 echocardiograms). Performance was compared against manual and Fetal Heart Quantification (Fetal HQ) measurements, and a dynamic Z-score model referencing gestational age and fetal biometrics was established. The AI achieved accurate segmentation, with mean Dice similarity coefficients >92% and mean intersection-over-union >85% across all test datasets. It exhibited higher intraclass correction coefficients and R-values relative to experts than inter-observer variability, alongside smaller mean absolute error and limits of agreement. The mean individual equivalence coefficients of all cardiac function parameters were below zero, indicating lower variability than manual or Fetal HQ. These results demonstrate that our fully automated AI workflow enables accurate, efficient, and reproducible quantification of fetal cardiac function, supporting its potential for standardized clinical application.
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Data availability
The data that support the findings of this study are available to qualified researchers on reasonable request from the corresponding authors. Please email the corresponding author Dr. Hongning Xie at xiehn@mail.sysu.edu.cn.
Code availability
The codes of the paper are available on reasonable request. Please email the corresponding author Dr. Hongning Xie at xiehn@mail.sysu.edu.cn.
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Acknowledgements
This study was supported by research grants from the National Scientific Foundation Committee of China (82171938, 81801705), the Guangdong Provincial Basic and Applied Basic Research Fund Project (2022A1515220200), the Guangzhou Science and Technology Program (2024A04J4569).
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C.H.: Writing-review & editing, Writing-original draft, Visualization, Validation, Formal analysis, Investigation, Data curation, Conceptualization; L.Z.: Writing-original draft, Visualization, Formal analysis, Data curation; B.X.: Writing-review & editing, Software, Investigation, Methodology, Conceptualization; Y.J.: Visualization, Formal analysis; Y.Z. and X.L.: Resources, Data curation; T.L.: Writing-review & editing, Funding acquisition; M.H.: Visualization, Formal analysis; Y.Y. and N.W.: Software, Methodology, Resources, Investigation, Data curation; H.X.: Writing-review & editing, Funding acquisition, Supervision, Conceptualization. C.H., L.Z. and B.X. are co-first authors of this work.
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Huang, C., Zhang, L., Xie, B. et al. Automated interpretation of fetal cardiac function evaluation from the echocardiogram. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02381-3
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DOI: https://doi.org/10.1038/s41746-026-02381-3
