ArticleFrontiers in artificial intelligence2021
A Machine Learning Approach to Monitor the Emergence of Late Intrauterine Growth Restriction.
Article in Frontiers in artificial intelligence, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 29 citations in OpenAlex.
- Artificial Intelligence in Prediction and Diagnosis of Fetal Growth Restriction: A Systematic Review.Indian journal of pediatrics · 2026Review
- Advancements in Fetal Heart Rate Monitoring: A Report on Opportunities and Strategic Initiatives for Better Intrapartum Care.BJOG : an international journal of obstetrics and gynaecology · 2025Review
- Current progress and future prospects of machine learning in the diagnosis of neonatal encephalopathy: a narrative review.Translational pediatrics · 2025Review
- Prediction of low birth weight from fetal ultrasound and clinical characteristics: a comparative study between a low- and middle-income and a high-income country.BMJ global health · 2024Article
- Fetal heart rate spectral analysis in raw signals and PRSA-derived curve: normal and pathological fetuses discrimination.Medical & biological engineering & computing · 2024Article
- Hybrid-FHR: a multi-modal AI approach for automated fetal acidosis diagnosis.BMC medical informatics and decision making · 2024Article
- Intelligent antepartum fetal monitoring via deep learning and fusion of cardiotocographic signals and clinical data.Health information science and systems · 2023Article
- RT-TRAQ: An algorithm for real-time tracking of faint quasi-periodic signals in noisy time series.Smart health (Amsterdam, Netherlands) · 2023Article
- Prediction Models for Intrauterine Growth Restriction Using Artificial Intelligence and Machine Learning: A Systematic Review and Meta-Analysis.Healthcare (Basel, Switzerland) · 2023Review
- Artificial intelligence in obstetrics.Obstetrics & gynecology science · 2022Article
- Fetal growth restriction and stillbirth: Biomarkers for identifying at risk fetuses.Frontiers in physiology · 2022Review
- Effects of Prenatal Exposure to Alcohol and Smoking on Fetal Heart Rate and Movement Regulation.Frontiers in physiology · 2021Article
- Using machine learning to identify risk factors for Alzheimer's disease among older adults in the United States: The role of chronic and behavioral health.Journal of Alzheimer's disease reportsArticle
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Late intrauterine growth restriction (IUGR) is a fetal pathological condition characterized by chronic hypoxia secondary to placental insufficiency, resulting in an abnormal rate of fetal growth. This pathology has been associated with increased fetal and neonatal morbidity and mortality. In standard clinical practice, late IUGR diagnosis can only be suspected in the third trimester and ultimately confirmed at birth. This study presents a radial basis function support vector machine (RBF-SVM) classification based on quantitative features extracted from fetal heart rate (FHR) signals acquired using routine cardiotocography (CTG) in a population of 160 healthy and 102 late IUGR fetuses. First, the individual performance of each time, frequency, and nonlinear feature was tested. To improve the unsatisfactory results of univariate analysis we firstly adopted a Recursive Feature Elimination approach to select the best subset of FHR-based parameters contributing to the discrimination of healthy vs. late IUGR fetuses. A fine tuning of the RBF-SVM model parameters resulted in a satisfactory classification performance in the training set (accuracy 0.93, sensitivity 0.93, specificity 0.84). Comparable results were obtained when applying the model on a totally independent testing set. This investigation supports the use of a multivariate approach for the
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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.