ArticleFrontiers in microbiology2022
Predicting preterm birth through vaginal microbiota, cervical length, and WBC using a machine learning model.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 36 citations in OpenAlex.
- Effects of emergency/nonemergency cervical cerclage on the vaginal microbiome of pregnant women with cervical incompetence.Frontiers in cellular and infection microbiology · 2023Trial
- Machine Learning Prediction of Vaginal Tissue Tears Using Finite Element Simulations Informed by Planar Biaxial Testing.Annals of biomedical engineering · 2026Article
- Urogenital immune signatures are associated with birth outcomes after maternal urinary tract infection.Science translational medicine · 2026Article
- Decoding the reproductive microbiome: enabling clinical and biological insights through machine and deep learning.Frontiers in endocrinology · 2026Review
- Vaginal microbiome and its implications in preterm birth.Obstetrics & gynecology science · 2025Review
- Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning.Scientific reports · 2025Article
- Review
- Machine learning prediction models for mode of delivery in prolonged pregnancies in Sweden.Scientific reports · 2025Article
- Article
- A Comparative Longitudinal Study Analyzing Vaginal Microbiota Differences Between Term and Preterm Pregnancies in Korean Women.Medicina (Kaunas, Lithuania) · 2025Article
- The single-cell immune profile throughout gestation and its potential value for identifying women at risk for spontaneous preterm birth.European journal of obstetrics & gynecology and reproductive biology: X · 2025Review
- The untapped potential of vaginal microbiome diagnostics for improving women's health.Frontiers in cellular and infection microbiology · 2025Review
- Insights into the alteration of vaginal microbiota and metabolites in pregnant woman with preterm delivery: prospective cohort study.Frontiers in cellular and infection microbiology · 2025Article
- Microbiomic insights into the unique effects of vaginal microbiota on preterm birth in Chinese pregnant women.Frontiers in microbiology · 2025Article
- The vaginal microbial signatures of preterm birth woman.BMC pregnancy and childbirth · 2024Article
- Predicting Spontaneous Preterm Birth Using the Immunome.Clinics in perinatology · 2024Review
- Strategies for Safeguarding High-Risk Pregnancies From Preterm Birth: A Narrative Review.Cureus · 2024Review
- DeepMPTB: a vaginal microbiome-based deep neural network as artificial intelligence strategy for efficient preterm birth prediction.Biomarker research · 2024Article
- Predicting risk of preterm birth in singleton pregnancies using machine learning algorithms.Frontiers in big data · 2024Article
- Ethnic disparity in diagnosing asymptomatic bacterial vaginosis using machine learning.NPJ digital medicine · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An association between the vaginal microbiome and preterm birth has been reported. However, in practice, it is difficult to predict premature birth using the microbiome because the vaginal microbial community varies highly among samples depending on the individual, and the prediction rate is very low. The purpose of this study was to select markers that improve predictive power through machine learning among various vaginal microbiota and develop a prediction algorithm with better predictive power that combines clinical information. As a multicenter case-control study with 150 Korean pregnant women with 54 preterm delivery group and 96 full-term delivery group, cervicovaginal fluid was collected from pregnant women during mid-pregnancy. Their demographic profiles (age, BMI, education level, and PTB history), white blood cell count, and cervical length were recorded, and the microbiome profiles of the cervicovaginal fluid were analyzed. The subjects were randomly divided into a training (
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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.