What question did this study set out to answer?

Evaluate the effectiveness of artificial intelligence models in predicting preterm birth using uterine activity.

February 12, 2026The Journal of Maternal-Fetal & Neonatal Medicine1 citationsOpen Access

Use of uterine activity to predict preterm birth by artificial intelligence assisted models: a narrative systematic review

JCJagrati ChopraPrincess Anne Hospital CMCharlotte MadenSouthampton General Hospital IHIsabel HowlettUniversity of Southampton

Key Points

Evaluate the effectiveness of artificial intelligence models in predicting preterm birth using uterine activity.
Conducted a narrative systematic review of existing literature
Assessed the performance of non-linear features and deep learning models
Analyzed the lack of external validation in current studies
Found no strong evidence for the applicability of current AI models in clinical settings
Identified a need for better integration of clinical data in research
Recommended increased focus on early detection of preterm birth in future studies

Abstract

Non-linear features and DL models offer superior results. However, we did not find evidence of external validation, thus the applicability of models in uterine contraction assessment for the prediction of PTB remains limited. Future research requires an emphasis on clinical data integration within high-quality studies, as well as more more studies focusing on early PTB detection.

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