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

Objective This retrospective study aims to develop and validate a multimodal nomogram for the prenatal risk assessment of HLHS and to explore significant risk factors for HLHS. Method This retrospective study enrolled 161 normal pregnancies and 52 pregnancies diagnosed with HLHS at the Ultrasound Diagnosis Department of Maternal and Child Health Hospital of Hubei Province in China from September 2019 to September 2023. Experienced sonographers selected standard four-chamber cardiac views and delineated the boundaries of the left and right atria and ventricles. A Resnet-like variational autoencoder (RVAE) was used to extract features from four-chamber views in a self-supervised learning strategy. These features were then converted into an image score. The proposed multimodal nomogram was developed using univariate and multivariate logistic regression analysis, incorporating the image score, demographics, and morphological characteristics of the fetus heart. To evaluate the nomogram's performance, we constructed a clinical regression model, four machine learning (ML) models, and an expert model comprising three sonographers with varying years of clinical experience. Results The study identified several significant risk factors for assessing the risk of HLHS, including the diameter of the left ventricle, the area ratio between the left and right atrium, the area ratio between the left and right ventricle, and the image score calculated from the four-chamber view. A multimodal nomogram was constructed based on these factors, achieving an accuracy of 0.935 and an AUC of 0.991. The performance of the nomogram was better than that of the traditional logistic regression and ML models, and comparable to that of the expert model. Additionally, the multimodal nomogram outperforms sonographers with 3-year and 6-year experience, and performs only slightly worse than the sonographer with 10-year experience. When combined with the heat-map generated from RVAE, the nomogram can serve as an easy-to-use tool to help clinicians better understand the process of computer-aided diagnosis. Conclusion The proposed multimodal nomogram demonstrates its superiority, effectiveness and interpretability in prenatal risk assessment of HLHS. Additionally, two risk factors, i.e., the area ratio between the left and right atrium and the area ratio between the ventricle, deserves further investigation during clinical diagnosis of HLHS.