ArticleScientific reports2020
Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980-2015.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06974188 (AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management), which is not on this map. Cited by 37 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management: A Randomized Controlled Trial (CURAte)
Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it, 76 citations in OpenAlex.
- Machine Learning for Predicting Stillbirth: A Systematic Review.Reproductive sciences (Thousand Oaks, Calif.) · 2025Pooled it
- On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature.BMC medical informatics and decision making · 2022Pooled it
- Machine Learning-Based First-Trimester Antenatal Risk Prediction for Adverse Maternal and Neonatal Outcomes: Multicenter Model Development Study.Journal of medical Internet research · 2026Article
- Machine learning to predict adverse perinatal outcomes: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- Genetics and Stillbirth.Clinics in perinatology · 2026Review
- Why current risk factor-based approaches fall short in predicting stillbirth: a national cohort study of nulliparous women in England.BMC medicine · 2026Article
- Prediction of stillbirth using machine learning methods.Scientific reports · 2025Article
- Machine learning prediction models for mode of delivery in prolonged pregnancies in Sweden.Scientific reports · 2025Article
- Machine learning for preventing stillbirths: is it possible to transform data into life-saving insights?BMC pregnancy and childbirth · 2025Article
- The Application of Machine Learning Models to Predict Stillbirths.Medicina (Kaunas, Lithuania) · 2025Article
- AI-based analysis of fetal growth restriction in a prospective obstetric cohort quantifies compound risks for perinatal morbidity and mortality and identifies previously unrecognized high risk clinical scenarios.BMC pregnancy and childbirth · 2025Observational
- The Role of Digital Technology in Preventing Anemia Among Pregnant Women: A Scoping Review.International journal of telemedicine and applications · 2025Review
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- Interpretable Predictive Models to Understand Risk Factors for Maternal and Fetal Outcomes.Journal of healthcare informatics research · 2024Article
- Risk prediction model based on machine learning for predicting miscarriage among pregnant patients with immune abnormalities.Frontiers in pharmacology · 2024Article
- Placental Cannabinoid Receptor Expression in Preterm Birth.Journal of pregnancy · 2024Observational
- Prediction Models for Adverse Pregnancy Outcomes in India: Methodological Considerations for an Emerging Topic.Journal of obstetrics and gynaecology of India · 2023Article
- Identifying risk of stillbirth using machine learning.American journal of obstetrics and gynecology · 2023Article
- Building a predictive model of low birth weight in low- and middle-income countries: a prospective cohort study.BMC pregnancy and childbirth · 2023Article
Corrections and comments
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Authors and funding
17 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression.
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