ArticleScientific reports2023
Predicting preterm birth using machine learning techniques in oral microbiome.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 11 citations in OpenAlex.
- From virtual pregnancy to digital twin obstetrics: multimodal data integration for personalized prediction of pregnancy complications.Frontiers in medicine · 2026Review
- The oral microbiome in relation to systemic maternal conditions and pregnancy complications: a systematic review and meta-analysis.Journal of oral microbiology · 2026Review
- The role of oral dysbiosis in pregnancy complications: a systematic review and meta-analysis of preterm birth.Journal of oral microbiology · 2026Review
- Oral, Vaginal, and Placental Microbiota Profiles in Japanese Pregnancies with Preterm Birth and Chronic Abruption-Oligohydramnios Sequence (CAOS): A Cross-Sectional Study.Medicina (Kaunas, Lithuania) · 2025Article
- Machine learning-augmented biomarkers in mid-pregnancy Down syndrome screening improve prediction of small-for-gestational-age infants.Orphanet journal of rare diseases · 2025Article
- Body site-specific micro- and lactobiota in genitourinary infections during pregnancy.Frontiers in cellular and infection microbiology · 2025Article
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Authors and funding
8 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Preterm birth prediction is essential for improving neonatal outcomes. While many machine learning techniques have been applied to predict preterm birth using health records, inflammatory markers, and vaginal microbiome data, the role of prenatal oral microbiome remains unclear. This study aimed to compare oral microbiome compositions between a preterm and a full-term birth group, identify oral microbiome associated with preterm birth, and develop a preterm birth prediction model using machine learning of oral microbiome compositions. Participants included singleton pregnant women admitted to Jeonbuk National University Hospital between 2019 and 2021. Subjects were divided into a preterm and a full-term birth group based on pregnancy outcomes. Oral microbiome samples were collected using mouthwash within 24 h before delivery and 16S ribosomal RNA sequencing was performed to analyze taxonomy. Differentially abundant taxa were identified using DESeq2. A random forest classifier was applied to predict preterm birth based on the oral microbiome. A total of 59 women participated in this study, with 30 in the preterm birth group and 29 in the full-term birth group. There was no significant difference in maternal clinical characteristics between the preterm and the full-birth group. Twenty-five differentially abundant taxa were identified, including 22 full-term birth-enriched taxa and 3 preterm birth-enriched taxa. The random forest classifier achieved high balanced accuracies (0.765 ± 0.071) using the 9 most important taxa. Our study identified 25 differentially abundant taxa that could differentiate preterm and full-term birth groups. A preterm birth prediction model was developed using machine learning of oral microbiome compositions in mouthwash samples. Findings of this study suggest the potential of using oral microbiome for predicting preterm birth. Further multi-center and larger studies are required to validate our results before clinical applications.
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