ReviewCurrent research in physiology2023
Machine learning and disease prediction in obstetrics.
Review in Current research in physiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 3 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 3 syntheses or guidelines pooled it, 34 citations in OpenAlex.
- Machine learning for predictive risk stratification in recurrent miscarriage: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Global performance of machine learning models to predict all-cause mortality: systematic review and meta-analysis.Scientific reports · 2025Pooled it
- Machine Learning for Predicting Stillbirth: A Systematic Review.Reproductive sciences (Thousand Oaks, Calif.) · 2025Pooled it
- Exploratory Non-invasive Estimation of Cervical Dilation from Electrohysterography and Maternal Data: A Machine-Learning Proof-of-Concept Study.Annals of biomedical engineering · 2026Article
- Machine Learning-Based Prediction of Fetal Macrosomia Using Maternal: A Pilot Study.Diagnostics (Basel, Switzerland) · 2026Article
- Racial Disparities and the Use of Artificial Intelligence for Predicting Maternal Mortality: A Literature Review.Epidemiologia (Basel, Switzerland) · 2026Review
- Review
- Development and validation of an interpretable machine learning model for predicting incident gestational hypothyroidism using clinical laboratory markers.Frontiers in medicine · 2026Article
- Machine learning based prediction of gestational diabetes mellitus using early pregnancy biomarkers and clinical data.Bioinformation · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Prediction of the Need for Surgical Evacuation in Patients with Incomplete Abortion.Risk management and healthcare policy · 2026Article
- The Impact of Artificial Intelligence on Women's Healthcare: A Systematic Review.Qatar medical journal · 2026Review
- Article
- Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence.Scientific reports · 2025Article
- Article
- Comparison and verification of detection accuracy for late deceleration with and without uterine contractions signals using convolutional neural networks.Frontiers in physiology · 2025Article
- A Decision Tree-Driven IoT systems for improved pre-natal diagnostic accuracy.BMC medical informatics and decision making · 2024Article
- Review
- Enhancing Fetal Anomaly Detection in Ultrasonography Images: A Review of Machine Learning-Based Approaches.Biomimetics (Basel, Switzerland) · 2023Review
- A Novel Predictive Machine Learning Model Integrating Cytokines in Cervical-Vaginal Mucus Increases the Prediction Rate for Preterm Birth.International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning technologies and translation of artificial intelligence tools to enhance the patient experience are changing obstetric and maternity care. An increasing number of predictive tools have been developed with data sourced from electronic health records, diagnostic imaging and digital devices. In this review, we explore the latest tools of machine learning, the algorithms to establish prediction models and the challenges to assess fetal well-being, predict and diagnose obstetric diseases such as gestational diabetes, pre-eclampsia, preterm birth and fetal growth restriction. We discuss the rapid growth of machine learning approaches and intelligent tools for automated diagnostic imaging of fetal anomalies and to asses fetoplacental and cervix function using ultrasound and magnetic resonance imaging. In prenatal diagnosis, we discuss intelligent tools for magnetic resonance imaging sequencing of the fetus, placenta and cervix to reduce the risk of preterm birth. Finally, the use of machine learning to improve safety standards in intrapartum care and early detection of complications will be discussed. The demand for technologies to enhance diagnosis and treatment in obstetrics and maternity should improve frameworks for patient safety and enhance clinical practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.