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Multimodal Nomogram for the Prenatal Risk Assessment of Hypoplastic Left Heart Syndrome Using Self-Supervised

Xinglong Wu1,2, Chen Cheng3, Zhenchun Ran1

  • 1School of Computer Science and Engineering, Hubei Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, People's Republic of China.

Journal of Ultrasound in Medicine : Official Journal of the American Institute of Ultrasound in Medicine
|May 9, 2026
PubMed
Summary

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This summary is machine-generated.

This study developed a multimodal nomogram for prenatal risk assessment of hypoplastic left heart syndrome (HLHS). The AI-powered tool accurately predicts HLHS risk, outperforming traditional methods and aiding clinical diagnosis.

Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect requiring early diagnosis.
  • Current prenatal diagnostic methods for HLHS have limitations in accuracy and accessibility.

Purpose of the Study:

  • To develop and validate a multimodal nomogram for prenatal risk assessment of HLHS.
  • To identify significant risk factors contributing to HLHS development.
  • To compare the nomogram's performance against existing models and expert sonographers.

Main Methods:

  • Retrospective analysis of 161 normal and 52 HLHS-diagnosed pregnancies.
  • Utilized a ResNet-like variational autoencoder (RVAE) for feature extraction from 4-chamber cardiac views.
Keywords:
deep learningfour‐chamber cardiac viewhypoplastic left heart syndromenomogramultrasound

Related Experiment Videos

  • Developed a multimodal nomogram integrating image scores, demographics, and fetal heart morphology via logistic regression.

    • Main Results:

    • The multimodal nomogram achieved an accuracy of 0.935 and an AUC of 0.991 for HLHS risk assessment.
    • Identified key risk factors: left ventricle diameter, left/right atrium area ratio, left/right ventricle area ratio, and RVAE image score.
    • The nomogram demonstrated superior performance compared to logistic regression and machine learning models, and was comparable to expert sonographers.

    Conclusions:

    • The developed multimodal nomogram is a superior, effective, and interpretable tool for prenatal HLHS risk assessment.
    • The left/right atrium area ratio and ventricle area ratio warrant further investigation in clinical HLHS diagnosis.