ArticleDiagnostics (Basel, Switzerland)2024
Prediction of Intrauterine Growth Restriction and Preeclampsia Using Machine Learning-Based Algorithms: A Prospective Study.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Abdominal landmark detection and classification of fetal growth conditions on obstetric ultrasound: a gestational age-conditioned multi-task network.Abdominal radiology (New York) · 2026Article
- Unsupervised Machine Learning for the Identification of Latent First-Trimester Obstetric Phenotypes Associated with Maternal and Perinatal Morbidity.Diagnostics (Basel, Switzerland) · 2026Article
- Digital and AI-assisted approaches across the preeclampsia care pathway: a scoping review.AJOG global reports · 2026Article
- Machine learning-augmented biomarkers in mid-pregnancy Down syndrome screening improve prediction of small-for-gestational-age infants.Orphanet journal of rare diseases · 2025Article
- Review
- Early-Onset Versus Late-Onset Preeclampsia in Bogotá, Colombia: Differential Risk Factor Identification and Evaluation Using Traditional Statistics and Machine Learning.Biomedicines · 2025Article
- The Role of Artificial Intelligence in Predicting the Progression of Intraocular Hypertension to Glaucoma.Life (Basel, Switzerland) · 2025Article
- Leveraging Machine Learning to Predict and Assess Disparities in Severe Maternal Morbidity in Maryland.Healthcare (Basel, Switzerland) · 2025Article
- Factors Associated with Maternal Morbidity in Patients with Eclampsia in Three Obstetric Intensive Care Units: A Retrospective Study.Journal of clinical medicine · 2024Article
- Predicting Intra- and Postpartum Hemorrhage through Artificial Intelligence.Medicina (Kaunas, Lithuania) · 2024Article
- Predicting Unfavorable Pregnancy Outcomes in Polycystic Ovary Syndrome (PCOS) Patients Using Machine Learning Algorithms.Medicina (Kaunas, Lithuania) · 2024Article
- Machine Learning-Based Algorithms for Enhanced Prediction of Local Recurrence and Metastasis in Low Rectal Adenocarcinoma Using Imaging, Surgical, and Pathological Data.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
(1) Background: Prenatal care providers face a continuous challenge in screening for intrauterine growth restriction (IUGR) and preeclampsia (PE). In this study, we aimed to assess and compare the predictive accuracy of four machine learning algorithms in predicting the occurrence of PE, IUGR, and their associations in a group of singleton pregnancies; (2) Methods: This observational prospective study included 210 singleton pregnancies that underwent first trimester screenings at our institution. We computed the predictive performance of four machine learning-based methods, namely decision tree (DT), naïve Bayes (NB), support vector machine (SVM), and random forest (RF), by incorporating clinical and paraclinical data; (3) Results: The RF algorithm showed superior performance for the prediction of PE (accuracy: 96.3%), IUGR (accuracy: 95.9%), and its subtypes (early onset IUGR, accuracy: 96.2%, and late-onset IUGR, accuracy: 95.2%), as well as their association (accuracy: 95.1%). Both SVM and NB similarly predicted IUGR (accuracy: 95.3%), while SVM outperformed NB (accuracy: 95.8 vs. 94.7%) in predicting PE; (4) Conclusions: The integration of machine learning-based algorithms in the first-trimester screening of PE and IUGR could improve the overall detection rate of these disorders, but this hypothesis should be confirmed in larger cohorts of pregnant patients from various geographical areas.
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