ArticlePloS one2023
Predicting preterm births from electrohysterogram recordings via deep learning.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 16 citations in OpenAlex.
- A deep learning method for preterm birth prediction using wavelet representations and auxiliary features from electrohysterogram.Medical & biological engineering & computing · 2026Article
- A multiscale PBPK-PD model of oxytocin-induced uterine excitation and contraction.The Journal of physiology · 2026Article
- Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.Frontiers in big data · 2024Article
- Article
- An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery.Scientific data · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
About one in ten babies is born preterm, i.e., before completing 37 weeks of gestation, which can result in permanent neurologic deficit and is a leading cause of child mortality. Although imminent preterm labor can be detected, predicting preterm births more than one week in advance remains elusive. Here, we develop a deep learning method to predict preterm births directly from electrohysterogram (EHG) measurements of pregnant mothers recorded at around 31 weeks of gestation. We developed a prediction model, which includes a recurrent neural network, to predict preterm births using short-time Fourier transforms of EHG recordings and clinical information from two public datasets. We predicted preterm births with an area under the receiver-operating characteristic curve (AUC) of 0.78 (95% confidence interval: 0.76-0.80). Moreover, we found that the spectral patterns of the measurements were more predictive than the temporal patterns, suggesting that preterm births can be predicted from short EHG recordings in an automated process. We show that preterm births can be predicted for pregnant mothers around their 31st week of gestation, prompting beneficial treatments to reduce the incidence of preterm births and improve their outcomes.
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Registered trials
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