Artificial Intelligence-Based Multimodal Ultrasound Model for Prediction of Spontaneous Preterm Birth: Development and Validation.

Authors

Han Bai, Hui Shen, Lihe Zhang, Qiao Zheng, Lihong Wu, Meifang Lin, Hongning Xie, Liu Du

Published in

Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine. Aug 11, 2026. Epub Aug 11, 2026.

Abstract

The primary aim of this study was to develop a multimodal artificial intelligence model for predicting spontaneous preterm birth using cervical 2-dimensional ultrasound and elastography images, as well as ultrasound parameters.
This prospective cohort study recruited pregnant women undergoing transvaginal ultrasound screening at 20-24 weeks of gestation. Participants were divided into spontaneous preterm birth group and full-term group. Three deep learning models were trained on images to compute scores via the best-performing model. Five supervised machine learning classifiers were developed and validated to predict spontaneous preterm birth based on ultrasound parameters and deep learning scores, and feature importance analysis was employed to visualize the importance of each feature in the model.
A total of 721 pregnant women (44 with spontaneous preterm birth) were included. In deep learning models based on ultrasound images, DenseNet121 (AUC: 0.92/0.93) model exhibited excellent performance, and corresponding scores were obtained. Elasticity contrast index, hardness ratio, external os stiffness, Ratio, 2-dimension-score, and elastography-score were included to construct the 5 machine learning models. Among the multimodal models, the neural network model showed the best predictive performance in the validation set (AUC = 0.85; 95% CI, 0.73-0.96). Feature importance analysis revealed that both elastography-score and 2-dimension-score made significant contributions in a majority of the models.
The multimodal artificial intelligence models using ultrasound parameters and cervical 2-dimensional ultrasound and elastography images demonstrated promising predictive capabilities in internal validation for spontaneous preterm birth in the second trimester. This offers assistance for intervention in patients at high risk of spontaneous preterm birth.

PMID:
42581620
Bibliographic data and abstract were imported from PubMed on 12 Aug 2026.

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