Use of uterine activity to predict preterm birth by artificial intelligence assisted models: a narrative systematic review
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.
Authors
- Adriane Chapman (ORCID: https://orcid.org/0000-0002-3814-2587)
- Helen Perry (ORCID: https://orcid.org/0000-0003-0334-045X)
- Jagrati Chopra
- Alexandra Kermack (ORCID: https://orcid.org/0000-0002-9176-9426)
- Kai Yang (ORCID: https://orcid.org/0000-0001-7497-3911)
- Charlotte Maden (ORCID: https://orcid.org/0009-0004-3116-5673)
- Mehtap Ozbey Arabaci
- Ying Cheong
- Diego Altamirano
- Isabel Howlett
Institutions
- University of Winchester (GB)
- Princess Anne Hospital (GB)
- National Institute for Health and Care Excellence (GB)
- Health and Care Research Wales (GB)
- University of Southampton (GB)
Publication Details
- Journal
- The Journal of Maternal-Fetal & Neonatal Medicine
- Published
- 2026-02-09
- DOI
- https://doi.org/10.1080/14767058.2025.2582375
- Primary Topic
- Preterm Birth and Chorioamnionitis
- Type
- article
- Field-Weighted Citation Impact
- 0.00