AIMC Topic: Pregnancy
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Predicting Risk for Patent Ductus Arteriosus in the Neonate: A Machine Learning Analysis.
Medicina (Kaunas, Lithuania)
Mar 26, 2025
: Patent ductus arteriosus (PDA) is common in newborns, being associated with high morbidity and mortality. While maternal and neonatal conditions are known contributors, few studies use advanced machine learning (ML) as predictive factors. This stud...
Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study.
Scientific reports
Mar 25, 2025
Preterm birth (PTB), defined as delivery before 37 weeks, affects 15 million infants annually, accounting for 11% of live births and over 35% of neonatal deaths. While advanced maternal age (≥ 35 years) is a known risk factor, PTB risk in women under...
The Artificial Intelligence-Enhanced Echocardiographic Detection of Congenital Heart Defects in the Fetus: A Mini-Review.
Medicina (Kaunas, Lithuania)
Mar 21, 2025
Artificial intelligence (AI) is rapidly gaining attention in radiology and cardiology for accurately diagnosing structural heart disease. In this review paper, we first outline the technical background of AI and echocardiography and then present an a...
A machine learning model based on placental magnetic resonance imaging and clinical factors to predict fetal growth restriction.
BMC pregnancy and childbirth
Mar 20, 2025
OBJECTIVES: To create a placental radiomics-clinical machine learning model to predict FGR.
Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer.
BMC pregnancy and childbirth
Mar 19, 2025
OBJECTIVE: This study aims to investigate the influencing factors of pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET) procedures in clinical practice. Several prediction models were constructed to predict pregnancy outcom...
Predicting low birth weight risks in pregnant women in Brazil using machine learning algorithms: data from the Araraquara cohort study.
BMC pregnancy and childbirth
Mar 19, 2025
BACKGROUND: Low birth weight (LBW) is a critical factor linked to neonatal morbidity and mortality. Early prediction is essential for timely interventions. This study aimed to develop and evaluate predictive models for LBW using machine learning algo...
Volume-based complete automation for ultrasound fetal biometry: A pilot approach to assess feasibility, reliability, and perspectives.
International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
Mar 18, 2025
BACKGROUND: Detection algorithms targeting anatomic landmarks in three-dimensional (3D) ultrasound (US) volume (three-dimensional US) appear to be a relevant and easy-to-implement option to address junior and occasional operators' difficulties in pro...
Visualized hysteroscopic artificial intelligence fertility assessment system for endometrial injury: an image-deep-learning study.
Annals of medicine
Mar 17, 2025
OBJECTIVE: Asherman's syndrome (AS) is a significant cause of subfertility in women from developing countries. Over 80% of AS cases in these regions are linked to dilation and curettage (D&C) procedures following pregnancy. The incidence of AS in pat...
Presenting a prediction model for HELLP syndrome through data mining.
BMC medical informatics and decision making
Mar 17, 2025
BACKGROUND: The HELLP syndrome represents three complications: hemolysis, elevated liver enzymes, and low platelet count. Since the causes and pathogenesis of HELLP syndrome are not yet fully known and well understood, distinguishing it from other pr...
Extraction of fetal heartbeat locations in abdominal phonocardiograms using deep attention transformer.
Computers in biology and medicine
Mar 16, 2025
Assessing fetal health traditionally involves techniques like echocardiography, which require skilled professionals and specialized equipment, making them unsuitable for low-resource settings. An emerging alternative is Phonocardiography (PCG), which...
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